Copenhagen Internal Data Review - Verson 1.3
Michael Fichman, Xiaxin Tang for PennPraxis & VibeLab
19 September, 2024
Summary
About this document and our methodology
This markdown document contains internal analysis of Creative Footprint Project Copenhagen data. PennPraxis combined CFP survey data with spatial and economic data from local and national sources to understand the relationships between the urban environment and its populations, and CFP venues and their characteristics. Copenhagen’s venue database and related urban data can now be compared to that of other CFP cities.
Any of the graphics or tables in this document can be rendered as vector files (pdf, eps) for use in a final report. They can be styled in Adobe Illustrator or other similar software in the graphic production process. The maps in this document are not heavily styled, as it is expected that the vectors will be manipulated into production quality once relevant graphics are selected for publication.
This document has an appendix outlining methodology and source documentation.
Top-Line Analytical Findings
Top takeaways:
Copenhagen has a very high concentration of venues per capita and per area - as high as any CFP city. These venues are spread widely across the city. Every district in the city has venues, which is unique among CFP cities.
The venue sample contains 108 venues, all of which are in the City of Copenhagen.
Panels of local experts gave Copenhagen’s venues very high ratings for programming quality. Venues were rated as being more focused on creativity and promotion of artistic content than in other CFP cities. This is particularly notable with “Legacy” venues - older, larger venues.
Copenhagen’s urban form, spatial economics, and the characteristics of the venues in the city follow similar patterns to other transit-oriented CFP cities, with a few exceptions.
Copenhagen’s venues are far less concentrated in the urban center than in peer cities. There are very distinct clusters across the city. Based on the community’s high rating of late night transportation access and Copenhagen’s reputation, this might be related to high quality urban accessibility. Widespread, accessible, multi-modal transit presents an opportunity to cultivate investments in programming without the relatively high costs usually associated with a venue’s location in a transit accessible space.
Copenhagen’s venue mix is healthy, but lacks spaces under 500 m^2 category. It has a high quantity of multi-use spaces (68%), and there is more Warehouse use than in other cities.
Presently, there is a good opportunity for intervention to preserve affordability - increases in rent over the last five years are lower than those observed in peer CFP cities such as Rotterdam. The Kommuneplan 2019 estimates substantial growth in the near future, and important areas such as Norrbro are forecast to experience gentrification - housing or development plans will need to keep up to maintain supply and affordability, and direct mainstream cultural offers to appropriate areas.
SAMPLE CHARACTERISTICS
The CFP sample consists of 108 cultural venues.
The scope of the geographic and economic analysis includes the City of Copenhagen’s 10 districts. It does not include other municipalities in the Capital Region.
VENUE CHARACTERISTICS
Program ratings were, on average, extraordinarily high, specifically with regards to Creative Output, Experimentation, and Promotion of artistic content.(Section 2.2) Copenhagen’s experimentation, and promotion ratings were the highest of any CFP cities, creative output was second to only Sydney. Community focus was second lowest of any city. There were notably few venues that were assessed poorly across all categories. Programming characteristics are generally similar between venue types, including venues that also have restaurant programming. There was some slightly higher assessment for clubs.
The venue sample venue sample is has a mix of venues of various sizes, but lacks venues under 100 m^2 - a critical part of a “healthy” venue ladder. (Section 1.2.1). The larger the venue, the less likely it is to have a community or experimentally-focused program (Section 2). We only have 8% of our venues in this smallest category, and this sample size is too small to see meaningful measurements about programming, but the global trend is that the smallest venues have the highest community programming ratings.
The sample is dominated by venues between 100 and 500 square meters (~34%), and large venues over 1000 square meters (36%).
Venue age profile is similar to other CFP cities (Section 1.2.4). Legacy venues (over 20+ years) tend to be in the center (Indry By and neighboring districts), but, the Copenhagen sample is notably not as center-heavy compared to other cities.
Multi-use venues are widespread, and there is notable over-representation of Warehouse, Studio, and Open Air uses compared to other CFP cities, and under-representation of restaurant uses. This suggests more of a content focus relative to other cities, where multi-functional restaurants host many music events.
Copenhagen’s venues fall into three general groups - Legacy Venues, Mainstream Venues, and Creative Engines. Copenhagen is unique in that older, larger “Legacy Venues” have very highly rated programming. Creative Engines - smaller, younger, more experimental venues - are (as is typical) located further outside the center. We categorized the venues using a machine learning classification algorithm.
VENUES AND THE CITY
GEOGRAPHY OF DISTRICTS Venues in Copenhagen are notably diffuse across the city. There are numerous venue-rich districts and clusters across the city. (Sections ????) Copenhagen has here is no single district with a density among the top 25 districts in the CFP sample. Indre By and Norrebro, the densest districts, are 1/2 as dense as Rotterdam’s Centrum (2020) or Berlin’s Friedrichshain-Kreuzberg (2017), and 1/4 as dense as New York’s East Village (2018). (Section 6.5)
Copenhagen is the most venue dense CFP city - it has as many venues per square kilometer or per person as any CFP city (Section 6.5). With the exception of Sydney, where the sampling area was only the densest subset of the metro region, Copenhagen has the highest per capita and per area density of venues in the CFP (but fewer than Nashville, which was assessed with a very similar methodology).
Copenhagen’s venues are notably closer to one another, on average, than any other CFP city. This perhaps reflects the human-scale that Copenhagen is known for - in its streetscaping, and in its transportation accessibility. (Section 6.5). This presents opportunities for nightlife management, which is most efficiently done by focusing on districts for matters of safety, circulation, lighting, sound etc.,
Venue densities, programming ratings and urban variables show similar correlations as other CFP cities. (Section 6.3). Copenhagen has a relatively “strong center” urban form - with transit and rents concentrated in the center - and venue density corresponds to transit density and higher local rents. Higher investments in programming tend to take place in areas where rents are relatively lower (and access is somewhat poorer). Copenhagen’s compact urban form and mass transit-centered transportation system makes it more similar to other European and Asian CFP cities and less so North American and Australian CFP cities. (Section 6.4.5).
Like many cities, the relatively higher rents of the city center (in this case Indre By) are associated with lower content ratings. The Norrebro district, though relatively central, has among the highest program ratings of any district (Section 5, Section 9) Notably, it lacks any Mainstream venues. Indre By - unlike many downtowns, has a substantial number of well regarded venues - largely the highly rated Legacy Venues.
Despite the relatively high cost of living in Copenhagen, property values are not rising as fast as in some peer cities recently researched by CFP. Incomes are rising faster than property values - it is unclear what this suggests about changes in the local economy. (Section 6.1)
District Profiles - The following districts are profiled by their economic and cultural characteristics in (Section 9):
- Indre By
- Norrebro
- Vesterbro / Kongens Enghave
- Osterbro
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
# ---- Load Packages ----
#list of packages
library(tidyverse)
library(data.table)
library(sf)
library(viridis)
library(viridisLite)
#library(translateR)
library(kableExtra)
library(leaflet)
library(leaflet.extras)
library(lubridate)
library(mapview)
library(spdep)
library(FNN)
# ---- Load MUSA 5080 functions -----
# Import MUSA 5080 functions
source("https://raw.githubusercontent.com/urbanSpatial/Public-Policy-Analytics-Landing/master/functions.r")
# ---- Load Graphic Palettes ----
# Load a ggplot theme and a color palette
plotTheme <- theme(
plot.title =element_text(size=12),
plot.subtitle = element_text(size=8),
plot.caption = element_text(size = 6),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 10),
# Set the entire chart region to blank
panel.background=element_blank(),
plot.background=element_blank(),
#panel.border=element_rect(colour="#F0F0F0"),
# Format the grid
panel.grid.major=element_line(colour="#D0D0D0",size=.75),
axis.ticks=element_blank())
mapTheme <- theme(plot.title =element_text(size=12),
plot.subtitle = element_text(size=8),
plot.caption = element_text(size = 6),
axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_line(colour = 'transparent'),
panel.grid.minor=element_blank(),
legend.direction = "vertical",
legend.position = "right",
plot.margin = margin(1, 1, 1, 1, 'cm'),
legend.key.height = unit(1, "cm"), legend.key.width = unit(0.2, "cm"))
palette <- c("#10142A", "#47E9B9", "#F55D60", "#71EA48", "#C148EA", "#EAC148")
viridisPalette <- c("#440154", "#73D055", "#F55D60", "#238A8D", "#FDE725")
CityPalette <- c("#A230C2", "#3AEAB8", "#F9C700", "#0069FC",
"#EF3340", "#bdbdbd", "#bcbddc", "#10142A") # Berlin, NYC, Tokyo, Stockholm, Montreal, Sydney# Read in d1_aggregates from elsewhere
d1_aggregates <- st_read("~/GitHub/CFP/unified__city_data/district_aggregates/d1_aggregates_11_16_22.geojson") %>%
st_as_sf(crs = 4326)
# Load in target city d2_aggregates... do a quick text parsing to remove unwanted text in d2_name
d2_aggregates <- st_read("~/GitHub/CFP/city_data_engineer/copenhagen_data_aggregation/output/Copenhagen_d2_aggregates.geojson") %>%
st_as_sf(crs = 4326) %>%
mutate(d2_name = str_replace(d2_name, "District - ", ""))
# Load the d2_aggregates from elsewhere
# REad in all the individual cities and bind together - go sf to data frame to sf if geojson has non 4326 EPSG
# Add any missing column names (e.g. Rotterdam)
d2_aggregates_all <- st_read("~/GitHub/CFP/city_data_engineer/berlin_data_aggregation/output/Berlin_d2_aggregates.geojson") %>%
st_as_sf(crs = 4326) %>%
rbind(., st_read("~/GitHub/CFP/city_data_engineer/montreal_data_aggregation/output/Montreal_d2_aggregates.geojson") %>%
st_as_sf(crs = 4326)) %>%
rbind(., st_read("~/GitHub/CFP/city_data_engineer/newyork_data_aggregation/output/NewYork_d2_aggregates.geojson") %>%
st_as_sf(crs = 4326)) %>%
rbind(., st_read("~/GitHub/CFP/city_data_engineer/sydney_data_aggregation/output/Sydney_d2_aggregates.geojson") %>%
st_as_sf(crs = 4326)) %>%
rbind(., st_read("~/GitHub/CFP/city_data_engineer/tokyo_data_aggregation/output/Tokyo_d2_aggregates.geojson") %>%
st_as_sf(crs = 4326)) %>%
rbind(., st_read("~/GitHub/CFP/city_data_engineer/stockholm_data_aggregation/output/Stockholm_d2_aggregates.geojson") %>%
as.data.frame() %>%
st_as_sf(crs = 3006) %>%
st_transform(4326)) %>%
rbind(., st_read("~/GitHub/CFP/city_data_engineer/rotterdam_data_aggregation/output/Rotterdam_d2_aggregates.geojson") %>%
as.data.frame() %>%
mutate(pop_men_t1 = NA, pop_women_t1 = NA, pop_men_t2 = NA, pop_women_t2 = NA) %>%
st_as_sf(crs = 3035) %>%
st_transform(crs = 4326))
# Read in venues
main_venue_data <- read.csv("~/GitHub/CFP/city_data_engineer/data_venues/output/main_venue_data_2024.csv") %>%
mutate(city = ifelse(city == "Solna", "Stockholm", city))
# Read in EPSG information
source("~/GitHub/CFP/unified__city_data/epsg_list.R")# Load a list of special wards, do a quick join to assign english names to the d1
# Perhaps this should get put in the district_aggregation code
# list of the GEOID for the 23 central wards of Tokyo that are included in the analysis.
special_wards <- c("13101",
"13102",
"13103",
"13104",
"13105",
"13106",
"13107",
"13108",
"13109",
"13110",
"13111",
"13112",
"13113",
"13114",
"13115",
"13116",
"13117",
"13118",
"13119",
"13120",
"13121",
"13122",
"13123")
d1_aggregates <- left_join(d1_aggregates %>%
mutate(d1_name_en = ifelse(city_en != "Tokyo", as.character(d1_name), NA),
d1_id = as.character(d1_id)),
as.data.frame(cbind(c("Chiyoda",
"Chuo",
"Minato",
"Shinjuku",
"Bunkyo",
"Taito",
"Sumida",
"Koto",
"Shinagawa",
"Meguro",
"Ota",
"Setagaya",
"Shibuya",
"Nakano",
"Suginami",
"Toshima",
"Kita",
"Arakawa",
"Itabashi",
"Nerima",
"Adachi",
"Katsu-Shika",
"Edo Gawa"),
special_wards)),
by = c("d1_id" = "special_wards")) %>%
mutate(d1_name_en = if_else(is.na(d1_name_en) == TRUE, as.character(V1),
d1_name_en))%>%
select(-V1)# Remove former arrondissements from d1 aggregates that are now towns since 2006 reorganization
# EXCEPT Mont Royal
remove_arrondissements <- c("Baie-d'Urfé",
"Beaconsfield",
"Senneville",
"Kirkland",
"Montréal-Est",
"Sainte-Anne-de-Bellevue",
"Dorval",
"Pointe-Claire")1 Venues
The data sample has 108 venues
main_venue_data %>% filter (city == "Copenhagen") %>% tally()## n
## 1 1081.1 Interactive Map
This interactive map shows all CFP venues - this is for internal use only.
Hover over any venue with your cursor to see some information about the data point.
Sources: CFP
l <- leaflet() %>%
addProviderTiles(providers$Esri.WorldTopoMap) %>%
setView(lng = mean(main_venue_data$x, na.rm = TRUE),
lat = mean(main_venue_data$y, na.rm = TRUE),
zoom = 2) %>%
addScaleBar(position = "topleft") %>%
addCircleMarkers(data= main_venue_data %>%
mutate(name = iconv(name, to = "UTF-8")),
lng=~x,
lat=~y,
radius =~ 1,
fillOpacity =~ 1,
color =~ "blue",
label=~paste(name, " | ", street, " | uid: ", uid, "| Year: ", year ))
l# Note the conversion above using iconv to get the Japanese characters to UTF-81.2 Venue Characteristics
1.2.1 Venue Size Chart
The venue “ladder” is dominated by 100-500 sqm (34%) and large venues (36%) but lacks small (sub 100 square meter) venues (just 8%). Such spaces are important for the development of local artists and community scenes.
The relatively small sample of sub 100 sqm venues means that cross tabulations about the nature of these spaces (programming, uses etc., are probably not useful).
Sources: CFP
ggplot(data = main_venue_data %>%
filter(size != 0,
is.na(city) == FALSE,
city == "Copenhagen") %>%
mutate(size = ifelse(size == 5, 4, size)) %>%
mutate(size = case_when(size == 1 ~ "1. < 100",
size == 2 ~ "2. 101-500",
size == 3 ~ "3. 501-1000",
size == 4 ~ "4. 1001+")) %>%
group_by(size) %>%
tally())+
geom_bar(aes(y = n, x = size), stat = 'identity',
fill = CityPalette[8], alpha = 0.6)+
#scale_fill_viridis_d()+
labs(
title = "Venue Size Distribution - Copenhagen (Square Meters)",
subtitle = "",
x="",
y="Total Venues",
#fill = "CFP City",
caption = "Data: CFP")+
plotTheme
1.2.2 Venue Size Table
main_venue_data %>%
filter(size != 0,
is.na(city) == FALSE,
city == "Copenhagen") %>%
mutate(size = ifelse(size == 5, 4, size)) %>%
mutate(size = case_when(size == 1 ~ "1. < 100 m2",
size == 2 ~ "2. 101-500 m2",
size == 3 ~ "3. 501-1000 m2",
size == 4 ~ "4. 1001+ m2")) %>%
group_by(size) %>%
tally() %>%
mutate(pct = round(100*(n/sum(n)), digits = 2)) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "650px", height = "400px")| size | n | pct |
|---|---|---|
|
9 | 8.33 |
|
37 | 34.26 |
|
23 | 21.30 |
|
39 | 36.11 |
1.2.3 Venue Size - Multi-City Comparison
The pattern we are observing is not uncommon - the distribution of sizes is similar to New York and Stockholm.
Sources: CFP
main_venue_data %>%
filter(size != 0,
is.na(city) == FALSE) %>%
mutate(size = as.numeric(size),
size = ifelse(size == 5, 4, size)) %>%
mutate(size = case_when(size == 1 ~ "1. < 100",
size == 2 ~ "2. 101-500",
size == 3 ~ "3. 501-1000",
size == 4 ~ "4. 1001+"),
city = case_when(city == "Berlin" ~ "1. BERLIN, 2017",
city == "New York" ~ "2. NEW YORK CITY, 2018",
city == "Tokyo" ~ "3. TOKYO, 2019",
city == "Stockholm" ~ "4. STOCKHOLM, 2021",
city == "Montreal" ~ "5. MONTREAL, 2022",
city == "Sydney" ~ "6. SYDNEY, 2023",
city == "Rotterdam" ~ "7. ROTTERDAM, 2024",
city == "Copenhagen" ~ "8. COPENHAGEN, 2025")) %>%
group_by(size, city) %>%
tally() %>%
ungroup() %>%
group_by(city) %>%
mutate(pct = 100*n/sum(n)) %>%
ggplot()+
geom_bar(aes(x = size, y = pct, fill = city),
stat = "identity", position = "dodge",
alpha = 0.6) +
scale_fill_manual(values = c(CityPalette[1], CityPalette[2], CityPalette[3],
CityPalette[4], CityPalette[5], CityPalette[6],
CityPalette[7], CityPalette[8]))+
facet_wrap(~city)+
theme(legend.direction = "horizontal", legend.position = "bottom")+
labs(
title = "Venue Size - CFP Cities",
subtitle = "",
x="Size (Square Meters)",
y="Percentage of Venues",
fill = "CFP City",
caption = "Data: CFP")+
plotTheme
#ggsave("rotterdam_images/multi_citysize.pdf", width = 8, height = 11, units = "in")1.2.3.2 Venue size Dynamic Map
Larger venues are more commonly found in outlying areas.
mapView(main_venue_data %>%
filter(city == "Copenhagen",
is.na(x) == FALSE, is.na(y) == FALSE) %>%
mutate(size = case_when(size == 1 ~ "1. < 100 m2",
size == 2 ~ "2. 101-500 m2",
size == 3 ~ "3. 501-1000 m2",
size == 4 ~ "4. 1001+ m2")) %>%
st_as_sf(coords = c("x", "y"), crs = 4326), zcol = "size" )1.2.4.2 Venue Size and Use Type
main_venue_data %>%
filter(size != 0,
is.na(city) == FALSE,
city == "Copenhagen") %>%
mutate(size = ifelse(size == 5, 4, size)) %>%
mutate(size = case_when(size == 1 ~ "1. < 100 m2",
size == 2 ~ "2. 101-500 m2",
size == 3 ~ "3. 501-1000 m2",
size == 4 ~ "4. 1001+ m2")) %>%
select(size, venueType_disco, venueType_club,
venueType_concertHall, venueType_musicBar,
venueType_restaurant, venueType_gallery)%>%
gather(-size, key = "variable", value = "value") %>%
filter(value != 0, is.na(value) == FALSE) %>%
group_by(size, variable) %>%
tally() %>%
ggplot()+
geom_bar(aes(y = n, x = size), stat = "identity",
alpha = 0.6, fill = CityPalette[7]) +
facet_wrap(~variable, scales = "free")+
labs(
title = "Venue Size by Use",
subtitle = "",
caption = "Data: CFP")+
ylab("Number of Venues")+
xlab("Size (Square Meters)")+
plotTheme
1.2.4 Venue Age Chart
There is a fairly healthy balance of venue age, similar to Sydney. Notably fewer 0-3 year old venues compared to other CFP cities.
Sources: CFP
ggplot(data = main_venue_data %>%
filter(is.na(years_operating) == FALSE,
years_operating != "",
is.na(city) == FALSE,
city == "Copenhagen") %>%
mutate(years_operating = as.numeric(years_operating)) %>%
mutate(years_operating = case_when(years_operating == 1 ~ "1. 0-3",
years_operating == 2 ~ "2. 4-10",
years_operating == 3 ~ "3. 11-20",
years_operating == 4 ~ "4. 20+")) %>%
filter(is.na(years_operating) == FALSE) %>%
group_by(years_operating) %>%
tally()%>%
mutate(pct = round(100*(n/sum(n)), digits = 2))%>%
ungroup())+
geom_bar(aes(y = n, x = years_operating), stat = 'identity',
fill = CityPalette[8], alpha = 0.6)+
#scale_fill_viridis_d()+
labs(
title = "Venue Age (Years)",
subtitle = "Data only available for x of y venues in sample",
x="",
y="Total Venues",
#fill = "CFP City",
caption = "Data: CFP")+
coord_flip()+
plotTheme
1.2.5 Venue Age Table
New element here - comparing Copenhagen to the three other post-pandemic CFP cities to compare age profiles. It’s not clear how well the pre-pandemic data relate - they are in very non-standard formats and the data quality is not clear. I’m pretty sure there is something wrong with the Tokyo data, specifically.
main_venue_data %>%
filter(is.na(years_operating) == FALSE,
years_operating != "",
is.na(city) == FALSE,
city == "Copenhagen") %>%
mutate(years_operating = as.numeric(years_operating)) %>%
mutate(years_operating = case_when(years_operating == 1 ~ "1. 0-3",
years_operating == 2 ~ "2. 4-10",
years_operating == 3 ~ "3. 11-20",
years_operating == 4 ~ "4. 20+")) %>%
group_by(years_operating) %>%
tally()%>%
mutate(pct = round(100*(n/sum(n)), digits = 2))%>%
ungroup() %>%
cbind(., main_venue_data %>%
filter(is.na(years_operating) == FALSE,
years_operating != "",
is.na(city) == FALSE,
city %in% c("Rotterdam", "Sydney", "Montreal")) %>%
mutate(years_operating = as.numeric(years_operating)) %>%
mutate(years_operating = case_when(years_operating == 1 ~ "1. 0-3",
years_operating == 2 ~ "2. 4-10",
years_operating == 3 ~ "3. 11-20",
years_operating == 4 ~ "4. 20+")) %>%
filter(is.na(years_operating) == FALSE) %>%
group_by(years_operating) %>%
tally()%>%
mutate(other_cfp_cities_pct = round(100*(n/sum(n)), digits = 2))%>%
ungroup() %>%
select(-years_operating, -n)) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "650px", height = "400px")| years_operating | n | pct | other_cfp_cities_pct |
|---|---|---|---|
|
13 | 12.15 | 15.99 |
|
37 | 34.58 | 27.24 |
|
23 | 21.50 | 18.80 |
|
34 | 31.78 | 37.96 |
Sources: CFP
main_venue_data %>%
filter(is.na(years_operating) == FALSE,
years_operating != "",
is.na(city) == FALSE) %>%
#mutate(years_operating = as.numeric(years_operating)) %>%
mutate(years_operating = case_when(years_operating %in% c("0", "0-3", "0-Jan", "1",
"1.0") ~ "1. 0-3",
years_operating %in% c("3-10", "2", "4-10",
"2.0") ~ "2. 4-10",
years_operating %in% c("3", "10-20", "11-20",
"3.0") ~ "3. 11-20",
years_operating %in% c("4", "20+", "4\n2",
"4.0") ~ "4. 20+")) %>%
mutate(years_operating = ifelse(city == "Tokyo" & year(ymd(year_opened)) < 1997,"4. 20+", years_operating)) %>%
filter(is.na(years_operating) == FALSE) %>%
mutate(city = case_when(city == "Berlin" ~ "1. BERLIN, 2017",
city == "New York" ~ "2. NEW YORK CITY, 2018",
city == "Tokyo" ~ "3. TOKYO, 2019",
city == "Stockholm" ~ "4. STOCKHOLM, 2021",
city == "Montreal" ~ "5. MONTREAL, 2022",
city == "Sydney" ~ "6. SYDNEY, 2023",
city == "Rotterdam" ~ "7. ROTTERDAM, 2024",
city == "Copenhagen" ~ "8. COPENHAGEN, 2024")) %>%
group_by(years_operating, city) %>%
tally()%>%
ungroup %>%
group_by(city) %>%
mutate(pct = round(100*(n/sum(n)), digits = 2))%>%
ungroup() %>%
ggplot()+
geom_bar(aes(x = years_operating, y = pct, fill = city),
stat = "identity", position = "dodge",
alpha = 0.6) +
scale_fill_manual(values = c(CityPalette[1], CityPalette[2], CityPalette[3],
CityPalette[4], CityPalette[5], CityPalette[6],
CityPalette[7], CityPalette[8]))+
facet_wrap(~city)+
theme(legend.direction = "horizontal", legend.position = "bottom")+
labs(
title = "Venue Age - CFP Cities",
subtitle = "",
x="Years operating",
y="Percentage of Venues",
fill = "CFP City",
caption = "Data: CFP")+
plotTheme
1.2.6 Venue Age Map
mapView(main_venue_data %>%
filter(city == "Copenhagen",
is.na(x) == FALSE, is.na(y) == FALSE) %>%
mutate(years_operating = case_when(years_operating %in% c("0", "0-3", "0-Jan", "1",
"1.0") ~ "1. 0-3",
years_operating %in% c("3-10", "2", "4-10",
"2.0") ~ "2. 4-10",
years_operating %in% c("3", "10-20", "11-20",
"3.0") ~ "3. 11-20",
years_operating %in% c("4", "20+", "4\n2",
"4.0") ~ "4. 20+")) %>%
st_as_sf(coords = c("x", "y"), crs = 4326), zcol = "years_operating" )2 Programming Variables
Sources: CFP
2.1 Programming Variables Overiview
Program ratings were, on average, extraordinarily high, specifically with regards to Creative Output, Experimentation, and Promotion of artistic content.
Some observations:
Copenhagen’s experimentation, and promotion ratings were the highest of any CFP cities, creative output was second to only Sydney. Community focus was second lowest of any city.
There were notably few venues that were assessed poorly across all categories.
Programming characteristics are generally similar between venue types, including venues that also have restaurant programming. There was some slightly higher assessment for clubs.
main_venue_data %>%
filter(city == "Copenhagen") %>%
select(experimentation, creative_output,
community_focus, promotion)%>%
gather(key = "variable", value = "value") %>%
filter(value != 0) %>%
mutate(variable = case_when(variable == "community_focus" ~ "Community Focus",
variable == "experimentation" ~ "Experimentation",
variable == "creative_output" ~ "Creative Output",
variable == "promotion" ~ "Promotion")) %>%
ggplot()+
geom_histogram(aes(value), position = "dodge", binwidth = 1, alpha = 0.6, fill = CityPalette[8]) +
facet_wrap(~variable)+
labs(
title = "Distribution of Programming Variables",
subtitle = "",
caption = "Data: CFP")+
ylab("Number of venues")+
xlab("Rating (1-4)")+
plotTheme
main_venue_data %>%
filter(city == "Copenhagen") %>%
mutate(cumulative_score = experimentation + creative_output +
community_focus+ promotion)%>%
ggplot()+
geom_histogram(aes(cumulative_score), position = "dodge", binwidth = 1, alpha = 0.6, fill = CityPalette[8]) +
labs(
title = "Cumulative Programming Variables by Venue",
subtitle = "Four categories scored 1-4",
caption = "Data: CFP")+
ylab("Number of venues")+
xlab("Cumulative rating")+
plotTheme
main_venue_data %>%
mutate(cumulative_score = experimentation + creative_output +
community_focus+ promotion)%>%
ggplot()+
geom_histogram(aes(cumulative_score), position = "dodge", binwidth = 1, alpha = 0.6, fill = CityPalette[8]) +
labs(
title = "Cumulative Programming Variables by Venue",
subtitle = "Four categories scored 1-4",
caption = "Data: CFP")+
ylab("Number of venues")+
xlab("Cumulative rating")+
facet_wrap(~city, scales = "free")+
plotTheme
Note - NYC and Berlin cumulative ratings are from 1-9 because Community Focus was not asked yet.
main_venue_data %>%
mutate(community_focus = ifelse(is.na(community_focus) == TRUE, 0, community_focus),
cumulative_score = experimentation + creative_output + community_focus + promotion,
mean_score = ifelse(city %in% c("Berlin", "New York"),
cumulative_score / 3, cumulative_score / 4 ))%>%
select(city, cumulative_score, mean_score) %>%
gather(-city, value = "value", key = "variable") %>%
group_by(city, variable) %>%
summarize(mean_score = mean(value, na.rm = TRUE),
score_sd = sd(value, na.rm = TRUE)) %>%
ggplot()+
geom_bar(aes(x = city, y = mean_score), stat = "identity")+
facet_wrap(~variable, scales = "free")+
plotTheme
main_venue_data %>%
select(city, experimentation, creative_output,
community_focus, promotion)%>%
gather(-city, key = "variable", value = "value") %>%
filter(value != 0) %>%
mutate(variable = case_when(variable == "community_focus" ~ "Community Focus",
variable == "experimentation" ~ "Experimentation",
variable == "creative_output" ~ "Creative Output",
variable == "promotion" ~ "Promotion")) %>%
ggplot()+
geom_density(aes(value, color = variable),
position = "dodge",
binwidth = 1, alpha = 0.6) +
facet_wrap(~city)+
labs(
title = "Distribution of Programming Variables",
subtitle = "",
caption = "Data: CFP")+
ylab("")+
xlab("Rating (1-4)")+
plotTheme
2.1.1. Programming ratings by venue use type
This is a new section - we look at each use type by program rating, and we also evaluate this against the entire CFP data set (with the modern collection protocol for use).
In Copenhagen (and across the data set), restaurants, discos and arenas have relatively low ratings across all indicators. Galleries, shops, studios - these have relatively higher indicators.
main_venue_data %>%
filter(city == "Copenhagen") %>%
select(experimentation, creative_output, community_focus,
promotion, contains("venueType")) %>%
pivot_longer(
cols = starts_with("venueType_"),
names_to = "venueType",
values_to = "count") %>%
filter(count > 0) %>%
group_by(venueType) %>%
summarize(mean_experimentation = round(mean(experimentation), digits = 1),
mean_community_focus = round(mean(community_focus), digits = 1),
mean_promotion = round(mean(promotion), digits = 1),
mean_creative_output = round(mean(creative_output), digits = 1)) %>%
gather(-venueType, key = "variable", value = "value") %>%
ggplot()+
geom_bar(aes(x = variable, y = value, fill = variable), stat = "identity") +
coord_flip()+
facet_wrap(~venueType)+
labs(
title = "Average Programming Rating by Use Type",
subtitle = "",
caption = "Data: CFP")+
xlab("Number of venues")+
ylab("Rating (1-4)")+
theme(legend.position = "none")+
plotTheme
main_venue_data %>%
filter(city == "Copenhagen") %>%
select(experimentation, creative_output, community_focus,
promotion, contains("venueType")) %>%
pivot_longer(
cols = starts_with("venueType_"),
names_to = "venueType",
values_to = "count") %>%
filter(count > 0) %>%
group_by(venueType) %>%
summarize(mean_experimentation = round(mean(experimentation), digits = 1),
mean_community_focus = round(mean(community_focus), digits = 1),
mean_promotion = round(mean(promotion), digits = 1),
mean_creative_output = round(mean(creative_output), digits = 1)) %>%
kable() %>%
kable_styling()| venueType | mean_experimentation | mean_community_focus | mean_promotion | mean_creative_output |
|---|---|---|---|---|
| venueType_arena | 1.2 | 1.2 | 3.0 | 3.2 |
| venueType_cinema | 2.3 | 2.7 | 3.3 | 3.0 |
| venueType_club | 3.1 | 3.1 | 3.7 | 3.7 |
| venueType_concertHall | 2.8 | 2.7 | 3.4 | 3.6 |
| venueType_disco | 1.3 | 1.6 | 2.1 | 1.9 |
| venueType_gallery | 3.7 | 3.1 | 3.6 | 3.9 |
| venueType_musicBar | 2.2 | 2.5 | 3.0 | 3.4 |
| venueType_openAir | 2.8 | 2.6 | 3.2 | 3.5 |
| venueType_restaurant | 2.7 | 2.8 | 2.8 | 3.0 |
| venueType_shop | 3.4 | 3.4 | 2.8 | 3.4 |
| venueType_studio | 3.3 | 3.2 | 3.3 | 3.5 |
| venueType_theater | 3.0 | 3.5 | 3.0 | 3.2 |
| venueType_warehouse | 2.7 | 2.4 | 2.7 | 3.0 |
main_venue_data %>%
filter(city %in% c("Sydney", "Montreal", "Rotterdam", "Stockholm", "Copenhagen" )) %>%
select(experimentation, creative_output, community_focus,
promotion, contains("venueType")) %>%
pivot_longer(
cols = starts_with("venueType_"),
names_to = "venueType",
values_to = "count") %>%
filter(count > 0) %>%
group_by(venueType) %>%
summarize(mean_experimentation = round(mean(experimentation, na.rm = TRUE), digits = 1),
mean_community_focus = round(mean(community_focus, na.rm = TRUE), digits = 1),
mean_promotion = round(mean(promotion, na.rm = TRUE), digits = 1),
mean_creative_output = round(mean(creative_output, na.rm = TRUE), digits = 1)) %>%
gather(-venueType, key = "variable", value = "value") %>%
ggplot()+
geom_bar(aes(x = variable, y = value, fill = variable), stat = "identity") +
coord_flip()+
facet_wrap(~venueType)+
labs(
title = "Average Programming Variables by Use Type - CFP Cities 2020-Present",
subtitle = "Current data collection protocol includes Stockholm, Montreal, Sydney, Rotterdam and Copenhagen",
caption = "Data: CFP")+
xlab("Number of venues")+
ylab("Rating (1-4)")+
theme(legend.position = "none")+
plotTheme
2.1.2 Cumulative Program Rating Map
main_venue_data %>%
filter(city == "Copenhagen",
is.na(x) == FALSE, is.na(y) == FALSE) %>%
mutate(cumulative_score = experimentation + creative_output +
community_focus+ promotion) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
mapView(., zcol = "cumulative_score" )+
mapview(d2_aggregates)2.2 Programming Summaries by City
Rotterdam was ranked poorly relative to other cities on average programming scores - having the lowest or second lowest ranking in every category of the 7 cities.
main_venue_data %>%
select(city, experimentation, creative_output,
community_focus, promotion)%>%
gather(-city, key = "variable", value = "value") %>%
filter(value != 0) %>%
mutate(variable = case_when(variable == "community_focus" ~ "Community Focus",
variable == "experimentation" ~ "Experimentation",
variable == "creative_output" ~ "Creative Output",
variable == "promotion" ~ "Promotion")) %>%
group_by(variable, city) %>%
summarize(mean = round(mean(value), digits = 1)) %>%
spread(city, mean) %>%
kable() %>%
kable_styling()| variable | Berlin | Copenhagen | Montreal | New York | Rotterdam | Stockholm | Sydney | Tokyo |
|---|---|---|---|---|---|---|---|---|
| Community Focus | NA | 2.5 | 2.7 | NA | 2.6 | 2.0 | 2.9 | 2.7 |
| Creative Output | 2.5 | 3.2 | 2.9 | 3.1 | 2.7 | 2.7 | 3.5 | 2.7 |
| Experimentation | 2.4 | 2.6 | 2.4 | 2.4 | 2.2 | 1.8 | 2.3 | 2.4 |
| Promotion | 2.5 | 3.0 | 2.9 | 2.7 | 2.1 | 2.2 | 2.8 | 3.2 |
main_venue_data %>%
select(city, experimentation, creative_output,
community_focus, promotion)%>%
gather(-city, key = "variable", value = "value") %>%
filter(value != 0) %>%
mutate(variable = case_when(variable == "community_focus" ~ "Community Focus",
variable == "experimentation" ~ "Experimentation",
variable == "creative_output" ~ "Creative Output",
variable == "promotion" ~ "Promotion"),
city = case_when(city == "Berlin" ~ "1. BERLIN, 2017",
city == "New York" ~ "2. NEW YORK CITY, 2018",
city == "Tokyo" ~ "3. TOKYO, 2019",
city == "Stockholm" ~ "4. STOCKHOLM, 2021",
city == "Montreal" ~ "5. MONTREAL, 2022",
city == "Sydney" ~ "6. SYDNEY, 2023",
city == "Rotterdam" ~ "7. ROTTERDAM, 2024",
city == "Copenhagen" ~ "8. COPENHAGEN, 2024")) %>%
group_by(variable, city) %>%
summarize(mean = round(mean(value), digits = 1)) %>%
ungroup() %>%
ggplot()+
geom_bar(aes(y = mean, x = city, fill = city), stat = "identity", alpha = 0.6)+
scale_fill_manual(values = CityPalette) +
facet_wrap(~variable)+
coord_flip()+
plotTheme
2.3 Experimental Program Measurements
Q: Compared to other venues in the city: Is this venue a platform for new and experimental trends, sounds and art forms? Is it a place for niche genres and experimental performers as well as extraordinary event concepts?
ggplot(data = main_venue_data %>%
filter(is.na(experimentation) == FALSE,
city == "Copenhagen"))+
geom_bar(aes(experimentation), fill = CityPalette[8], alpha = 0.6, size = 3)+
#scale_fill_viridis_d()+
labs(
title = "Likelihood of experimental program",
subtitle = "1 = Not at all likely -> 4 = Very likely",
x="Experimentation Score (low to high)",
y="Total Venues",
#fill = "CFP City",
caption = "Data: CFP")+
plotTheme
2.3.1 Cross-tabs - Experimentation by size chart
The overall pattern holds amongst venue size categories.
ggplot(data = main_venue_data %>%
filter(city == 'Copenhagen',
is.na(size) == FALSE,
size != "",
experimentation > 0) %>%
mutate(experimentation = case_when(experimentation == 1 ~ "1. Not At All",
experimentation == 2 ~ "2. Not too",
experimentation == 3 ~ "3. Somewhat",
experimentation == 4 ~ "4. Very"),
size = case_when(size == 1 ~ "1. < 100 m^2",
size == 2 ~ "2. 101-500 m^2",
size == 3 ~ "3. 501-1000 m^2",
size %in% c(4, 5) ~ "4. 1001 m^2+")) %>%
group_by(size, experimentation) %>%
tally()%>%
ungroup() %>%
group_by(size) %>%
mutate(pct = round(100*(n/sum(n)), digits = 2)))+
geom_bar(aes(y = n, x = size, fill = experimentation),
stat = 'identity', position = "dodge",
alpha = 0.6)+
scale_fill_viridis_d(guide = guide_legend())+
theme(legend.direction = "horizontal", legend.position = "bottom")+
labs(
title = "EXPERIMENTAL CONTENT SCORES BY VENUE SIZE",
subtitle = "Venues of all sizes rate low for experimental programming",
x="",
y="Number of venues in size category",
fill = "LIKELIHOOD OF EXPERIMENTAL CONTENT",
caption = "Data: CFP"
)+
plotTheme
#ggsave("sydney_images/experimentation_size.png")2.3.2 Cross-tabs - Experimentation by size table
We see basically the same shake-out here as we see in other cities.
main_venue_data %>%
filter(city == 'Copenhagen',
is.na(size) == FALSE,
experimentation > 0) %>%
mutate(size = case_when(size == 1 ~ "1. < 100 m^2",
size == 2 ~ "2. 101-500 m^2",
size == 3 ~ "3. 501-1000 m^2",
size %in% c(4, 5) ~ "4. 1001 m^2+")) %>%
group_by(size) %>%
summarize(count=n(),
mean_experimentation = round(mean(experimentation, na.rm = TRUE),
digits = 2))%>%
ungroup() %>%
cbind(., main_venue_data %>%
filter(is.na(size) == FALSE,
size != "",
experimentation > 0) %>%
mutate(
size = case_when(size == 1 ~ "1. < 100 m^2",
size == 2 ~ "2. 101-500 m^2",
size == 3 ~ "3. 501-1000 m^2",
size %in% c(4, 5) ~ "4. 1001 m^2+")) %>%
filter(is.na(size) == FALSE) %>%
group_by(size) %>%
summarize(cfp_average = round(mean(experimentation, na.rm = TRUE), digits = 2)) %>%
select(-size)) %>%
kable() %>%
kable_styling()| size | count | mean_experimentation | cfp_average |
|---|---|---|---|
|
9 | 2.78 | 2.39 |
|
37 | 2.73 | 2.35 |
|
23 | 2.52 | 2.43 |
|
39 | 2.46 | 2.25 |
2.2.3 Cross-tabs - Experimentation by age table
main_venue_data %>%
filter(city == 'Copenhagen',
is.na(size) == FALSE,
experimentation > 0) %>%
mutate(years_operating = as.numeric(years_operating)) %>%
mutate(years_operating = case_when(years_operating == 1 ~ "1. 0-3",
years_operating == 2 ~ "2. 4-10",
years_operating == 3 ~ "3. 11-20",
years_operating == 4 ~ "4. 20+")) %>%
filter(is.na(years_operating) == FALSE) %>%
group_by(years_operating) %>%
summarize(count=n(),
mean_experimentation = round(mean(experimentation, na.rm = TRUE),
digits = 2))%>%
ungroup() %>%
kable() %>%
kable_styling()| years_operating | count | mean_experimentation |
|---|---|---|
|
13 | 2.77 |
|
37 | 2.62 |
|
23 | 2.52 |
|
34 | 2.59 |
2.4. Creative Output
Q: Do artists in this venue perform live sets and/or original works? If DJing, is it performed to a level of artistic merit?
2.4.1 Cross-tabs - Creative Output by size chart
Interestingly, much higher creative content scores further down in the venue ladder, size-wise.
ggplot(data = main_venue_data %>%
filter(city == 'Copenhagen',
is.na(size) == FALSE,
size != "",
creative_output > 0) %>%
mutate(creative_output = case_when(creative_output == 1 ~ "1. Not At All",
creative_output == 2 ~ "2. Not too",
creative_output == 3 ~ "3. Somewhat",
creative_output == 4 ~ "4. Very"),
size = case_when(size == 1 ~ "1. < 100 m^2",
size == 2 ~ "2. 101-500 m^2",
size == 3 ~ "3. 501-1000 m^2",
size %in% c(4, 5) ~ "4. 1001 m^2+")) %>%
group_by(size, creative_output) %>%
tally()%>%
ungroup() %>%
group_by(size) %>%
mutate(pct = round(100*(n/sum(n)), digits = 2)))+
geom_bar(aes(y = n, x = size, fill = creative_output),
stat = 'identity', position = "dodge",
alpha = 0.6)+
scale_fill_viridis_d(guide = guide_legend())+
theme(legend.direction = "horizontal", legend.position = "bottom")+
labs(
title = "Creative Output by Venue Size",
subtitle = "Subtitle",
x="",
y="Number of venues in size category",
fill = "Likelihood of Original Creative Content",
caption = "Data: CFP"
)+
plotTheme
#ggsave("sydney_images/creativeOutput.png")2.4.3 Cross-tabs - Creative Output by age table
main_venue_data %>%
filter(city == 'Copenhagen',
is.na(size) == FALSE,
creative_output > 0) %>%
mutate(years_operating = as.numeric(years_operating)) %>%
mutate(years_operating = case_when(years_operating == 1 ~ "1. 0-3",
years_operating == 2 ~ "2. 4-10",
years_operating == 3 ~ "3. 11-20",
years_operating == 4 ~ "4. 20+")) %>%
filter(is.na(years_operating) == FALSE) %>%
group_by(years_operating) %>%
summarize(count=n(),
mean_creative_output = round(mean(creative_output, na.rm = TRUE),
digits = 2))%>%
ungroup() %>%
kable() %>%
kable_styling()| years_operating | count | mean_creative_output |
|---|---|---|
|
13 | 3.23 |
|
37 | 2.84 |
|
23 | 3.26 |
|
34 | 3.59 |
2.4.2 Cross-tabs - Creative Output by size table
main_venue_data %>%
filter(city == 'Copenhagen',
is.na(size) == FALSE,
creative_output > 0) %>%
mutate(size = case_when(size == 1 ~ "1. < 100 m^2",
size == 2 ~ "2. 101-500 m^2",
size == 3 ~ "3. 501-1000 m^2",
size %in% c(4, 5) ~ "4. 1001 m^2+")) %>%
group_by(size) %>%
summarize(count=n(),
mean_creative_output = round(mean(creative_output, na.rm = TRUE),
digits = 2))%>%
ungroup() %>%
kable() %>%
kable_styling()| size | count | mean_creative_output |
|---|---|---|
|
9 | 3.56 |
|
37 | 3.43 |
|
23 | 3.04 |
|
39 | 3.05 |
2.5. Community Focused Program Measurements
Q: Is the venue a consistent and regular platform for a niche genre and a stage for its emerging acts? Is it a hub for certain marginalised and/or underrepresented groups, scenes, milieus or is it a hotspot for the immediate neighbourhood to mingle? Is it known as an inclusive space for LGBTQIA+ artists/performers and audiences? Do venues platform other specific communities? Do they strive to create an inclusive environment?
ggplot(data = main_venue_data %>%
filter(is.na(community_focus) == FALSE,
city == "Copenhagen"))+
geom_bar(aes(community_focus), fill = CityPalette[8], alpha = 0.6, size = 3)+
#scale_fill_viridis_d()+
labs(
title = "Likelihood of community focused program",
subtitle = "1 = Not at all likely -> 4 = Very likely",
x="Community Focus Score (low to high)",
y="Total Venues",
#fill = "CFP City",
caption = "Data: CFP")+
plotTheme
2.5.1 Cross-tabs - Community Focus by size chart
The largest venues are least likely to be community focused. No surprises there.
ggplot(data = main_venue_data %>%
filter(city == 'Copenhagen',
is.na(size) == FALSE,
size != "",
community_focus > 0) %>%
mutate(community_focus = case_when(community_focus == 1 ~ "1. Not At All",
community_focus == 2 ~ "2. Not too",
community_focus == 3 ~ "3. Somewhat",
community_focus == 4 ~ "4. Very"),
size = case_when(size == 1 ~ "1. < 100 m^2",
size == 2 ~ "2. 101-500 m^2",
size == 3 ~ "3. 501-1000 m^2",
size %in% c(4, 5) ~ "4. 1001 m^2+")) %>%
group_by(size, community_focus) %>%
tally()%>%
ungroup() %>%
group_by(size) %>%
mutate(pct = round(100*(n/sum(n)), digits = 2)))+
geom_bar(aes(y = n, x = size, fill = community_focus),
stat = 'identity', position = "dodge",
alpha = 0.6)+
scale_fill_viridis_d(guide = guide_legend())+
theme(legend.direction = "horizontal", legend.position = "bottom")+
labs(
title = "Community Focus by Venue Size",
subtitle = "",
x="",
y="Percentage of venues in size category",
fill = "Likelihood of Community Focused Program",
caption = "Data: CFP"
)+
plotTheme
2.5.2 Cross-tabs - Community Focus by size table
main_venue_data %>%
filter(city == 'Copenhagen',
is.na(size) == FALSE,
community_focus > 0) %>%
mutate(size = case_when(size == 1 ~ "1. < 100 m^2",
size == 2 ~ "2. 101-500 m^2",
size == 3 ~ "3. 501-1000 m^2",
size %in% c(4, 5) ~ "4. 1001 m^2+")) %>%
group_by(size) %>%
summarize(count=n(),
mean_community_focus = round(mean(community_focus, na.rm = TRUE),
digits = 2))%>%
ungroup() %>%
kable() %>%
kable_styling()| size | count | mean_community_focus |
|---|---|---|
|
9 | 2.78 |
|
37 | 2.84 |
|
23 | 2.61 |
|
39 | 2.10 |
2.5.3 Cross-tabs - Community Focus by age table
main_venue_data %>%
filter(city == 'Copenhagen',
is.na(size) == FALSE,
community_focus > 0) %>%
mutate(years_operating = as.numeric(years_operating)) %>%
mutate(years_operating = case_when(years_operating == 1 ~ "1. 0-3",
years_operating == 2 ~ "2. 4-10",
years_operating == 3 ~ "3. 11-20",
years_operating == 4 ~ "4. 20+")) %>%
filter(is.na(years_operating) == FALSE) %>%
group_by(years_operating) %>%
summarize(count=n(),
mean_community_focus = round(mean(community_focus, na.rm = TRUE),
digits = 2))%>%
ungroup() %>%
kable() %>%
kable_styling()| years_operating | count | mean_community_focus |
|---|---|---|
|
13 | 2.54 |
|
37 | 2.30 |
|
23 | 2.61 |
|
34 | 2.71 |
What is the global trend across the data set?
ALL CITIES
main_venue_data %>%
filter(is.na(size) == FALSE,
size != "",
community_focus > 0) %>%
mutate(
size = case_when(size == 1 ~ "1. < 100 m^2",
size == 2 ~ "2. 101-500 m^2",
size == 3 ~ "3. 501-1000 m^2",
size %in% c(4, 5) ~ "4. 1001 m^2+")) %>%
group_by(size) %>%
summarize(mean_community_focus = round(mean(community_focus, na.rm = TRUE), digits = 2)) %>%
kable() %>%
kable_styling()| size | mean_community_focus |
|---|---|
|
2.83 |
|
2.66 |
|
2.67 |
|
2.26 |
| NA | 3.00 |
2.6. Promotion Program Measurements
Q: Is the promotion/marketing of this space focused on artistic content (artists, lineups, performances)? Are musicians the main reason why people attend these venues, and not e.g. culinary offers?
Copenhagen is very highly rated in this component, especially amongst the oldest category of venues and the small/medium size range.
ggplot(data = main_venue_data %>%
filter(is.na(promotion) == FALSE,
city == "Copenhagen"))+
geom_bar(aes(promotion), fill = CityPalette[8], alpha = 0.6, size = 3)+
#scale_fill_viridis_d()+
labs(
title = "Likelihood of promotion focused on artistic content",
subtitle = "1 = Not at all likely -> 4 = Very likely",
x="Promotional Score (low to high)",
y="Total Venues",
#fill = "CFP City",
caption = "Data: CFP")+
plotTheme
2.6.1 Cross-tabs - Promotional Focus on Artistic Content by size chart
Artistic content promotion is notably highly rated here, especially amongst older venues.
ggplot(data = main_venue_data %>%
filter(city == 'Copenhagen',
is.na(size) == FALSE,
size != "",
promotion > 0) %>%
mutate(promotion = case_when(promotion == 1 ~ "1. Not At All",
promotion == 2 ~ "2. Not too",
promotion == 3 ~ "3. Somewhat",
promotion == 4 ~ "4. Very"),
size = case_when(size == 1 ~ "1. < 100 m^2",
size == 2 ~ "2. 101-500 m^2",
size == 3 ~ "3. 501-1000 m^2",
size %in% c(4, 5) ~ "4. 1001 m^2+")) %>%
group_by(size, promotion) %>%
tally()%>%
ungroup() %>%
group_by(size) %>%
mutate(pct = round(100*(n/sum(n)), digits = 2)))+
geom_bar(aes(y = n, x = size, fill = promotion),
stat = 'identity', position = "dodge",
alpha = 0.6)+
scale_fill_viridis_d(guide = guide_legend())+
theme(legend.direction = "horizontal", legend.position = "bottom")+
labs(
title = "Likelihood Of Promotion of Artistic Content by Venue Size",
subtitle = "",
x="",
y="Percentage of venues in size category",
fill = "Likelihood of Artistic Promotion",
caption = "Data: CFP"
)+
plotTheme
2.5.2 Cross-tabs - Community Focus by size table
main_venue_data %>%
filter(city == 'Copenhagen',
is.na(size) == FALSE,
promotion > 0) %>%
mutate(size = case_when(size == 1 ~ "1. < 100 m^2",
size == 2 ~ "2. 101-500 m^2",
size == 3 ~ "3. 501-1000 m^2",
size %in% c(4, 5) ~ "4. 1001 m^2+")) %>%
group_by(size) %>%
summarize(count=n(),
mean_promotion = round(mean(promotion, na.rm = TRUE),
digits = 2))%>%
ungroup() %>%
kable() %>%
kable_styling()| size | count | mean_promotion |
|---|---|---|
|
9 | 3.33 |
|
37 | 3.16 |
|
23 | 3.04 |
|
39 | 2.67 |
2.5.3 Cross-tabs - Promotional Focus by age table
main_venue_data %>%
filter(city == 'Copenhagen',
is.na(size) == FALSE,
promotion > 0) %>%
mutate(years_operating = as.numeric(years_operating)) %>%
mutate(years_operating = case_when(years_operating == 1 ~ "1. 0-3",
years_operating == 2 ~ "2. 4-10",
years_operating == 3 ~ "3. 11-20",
years_operating == 4 ~ "4. 20+")) %>%
filter(is.na(years_operating) == FALSE) %>%
group_by(years_operating) %>%
summarize(count=n(),
mean_promotion = round(mean(promotion, na.rm = TRUE),
digits = 2))%>%
ungroup() %>%
kable() %>%
kable_styling()| years_operating | count | mean_promotion |
|---|---|---|
|
13 | 2.92 |
|
37 | 2.65 |
|
23 | 3.00 |
|
34 | 3.32 |
3 Interdisciplinarity Scores
Roughly 68% of venues have 2+ program uses. This is relatively high, but not the highest we have seen.
This trend holds up for all the size categories (didn’t make this chart but I looked into it - it’s true)
Sources: CFP
#either cut or perhaps change to a bar chart
main_venue_data %>%
filter(city == "Copenhagen") %>%
#mutate(venueType_cinema = as.numeric(ifelse(venueType_cinema == "0", 0, NA))) %>%
#mutate_at(vars(venueType_disco:venueType_studio), as.numeric) %>%
mutate(number_of_uses = rowSums(across(venueType_disco:venueType_studio))) %>%
group_by(number_of_uses) %>%
tally() %>%
mutate(percentage = round(100*(n/sum(n)), digits = 2)) %>%
kable() %>%
kable_styling()| number_of_uses | n | percentage |
|---|---|---|
| 0 | 4 | 3.70 |
| 1 | 31 | 28.70 |
| 2 | 40 | 37.04 |
| 3 | 17 | 15.74 |
| 4 | 11 | 10.19 |
| 5 | 3 | 2.78 |
| 6 | 1 | 0.93 |
| 7 | 1 | 0.93 |
3.1 Interdisciplinarity Content
Programming ratings are, on average, lower for single-use venues. This aligns with observed trends in other cities.
main_venue_data %>%
filter(city == "Copenhagen") %>%
mutate(number_of_uses = rowSums(across(venueType_disco:venueType_studio))) %>%
mutate(multi_use = ifelse(number_of_uses > 1, "Multi-Use", "Single-Use")) %>%
select(multi_use, experimentation, creative_output,
community_focus, promotion)%>%
gather(-multi_use, key = "variable", value = "value") %>%
filter(value != 0) %>%
mutate(variable = case_when(variable == "community_focus" ~ "Community Focus",
variable == "experimentation" ~ "Experimentation",
variable == "creative_output" ~ "Creative Output",
variable == "promotion" ~ "Promotion")) %>%
ggplot()+
geom_histogram(aes(value), position = "dodge", binwidth = 1, alpha = 0.6, fill = CityPalette[8]) +
facet_grid(multi_use~variable)+
labs(
title = "Distribution of Programming Variables",
subtitle = "",
caption = "Data: CFP")+
ylab("Number of venues")+
xlab("Rating (1-4)")+
plotTheme
main_venue_data %>%
filter(city %in% c("Tokyo", "Stockholm", "Montreal", "Sydney", "Rotterdam", "Copenhagen")) %>%
mutate(number_of_uses = rowSums(across(venueType_disco:venueType_studio ))) %>%
mutate(multi_use = ifelse(number_of_uses > 1, "Multi-Use", "Single-Use")) %>%
select(multi_use, experimentation, creative_output,
community_focus, promotion, city)%>%
gather(-multi_use, -city, key = "variable", value = "value") %>%
filter(value != 0) %>%
mutate(variable = case_when(variable == "community_focus" ~ "Community Focus",
variable == "experimentation" ~ "Experimentation",
variable == "creative_output" ~ "Creative Output",
variable == "promotion" ~ "Promotion")) %>%
group_by(variable, city, multi_use) %>%
summarize(mean_value = round(mean(value, na.rm = TRUE), digits = 2)) %>%
ggplot()+
geom_bar(aes(x= variable, y=mean_value, fill = multi_use), stat = "identity", position = "dodge", alpha = 0.6) +
facet_wrap(~city)+
labs(
title = "Distribution of Programming Variables",
subtitle = "",
caption = "Data: CFP")+
ylab("Mean Score")+
xlab("Programming Variable")+
plotTheme
3.2. Multi-Use Space Analysis
What are the most common combinations of uses?
What is going on with Warehouse here?
main_venue_data %>%
filter(city == "Copenhagen") %>%
select(matches("venueType")) %>%
mutate(across(everything(), ~ ifelse(. == 1, cur_column(), ""))) %>%
mutate(across(everything(), ~ str_replace(., "venueType_", ""))) %>%
unite("use_combo", everything(), sep = ",") %>%
mutate(use_combo = toupper(use_combo)) %>%
group_by(use_combo) %>%
tally() %>%
arrange(-n) %>%
kable() %>%
kable_styling()| use_combo | n |
|---|---|
| ,,,,,,,,,WAREHOUSE,,, | 7 |
| ,,,,,,MUSICBAR,,,,,, | 7 |
| ,,,CONCERTHALL,,,,,,,,, | 7 |
| ,,,CONCERTHALL,,,,,,WAREHOUSE,,, | 5 |
| ,DISCO,,,,,,,,,,, | 5 |
| CLUB,,,,,,,,,,,, | 4 |
| ,,,,,,,,,WAREHOUSE,,,STUDIO | 3 |
| ,,,,,,MUSICBAR,,RESTAURANT,,,, | 3 |
| ,,OPENAIR,CONCERTHALL,,,,,,,,, | 3 |
| ,,,,,,,,RESTAURANT,WAREHOUSE,,, | 2 |
| ,,,,,,,GALLERY,RESTAURANT,,,, | 2 |
| ,,,CONCERTHALL,,,,,,,ARENA,, | 2 |
| ,,,CONCERTHALL,,,,,,WAREHOUSE,,,STUDIO | 2 |
| ,,,CONCERTHALL,,,,,,WAREHOUSE,ARENA,, | 2 |
| ,,,CONCERTHALL,,,,,RESTAURANT,WAREHOUSE,,,STUDIO | 2 |
| ,,,CONCERTHALL,,,MUSICBAR,,,,,, | 2 |
| ,,,CONCERTHALL,THEATER,,,,,,,, | 2 |
| ,,OPENAIR,,,,,,,WAREHOUSE,,, | 2 |
| CLUB,,,CONCERTHALL,,,,,,,,, | 2 |
| CLUB,,,CONCERTHALL,,,,,,,,,STUDIO | 2 |
| ,,,,,,,,,,,SHOP,STUDIO | 1 |
| ,,,,,,,,RESTAURANT,,,, | 1 |
| ,,,,,,,,RESTAURANT,,,,STUDIO | 1 |
| ,,,,,,,,RESTAURANT,WAREHOUSE,,SHOP,STUDIO | 1 |
| ,,,,,,,GALLERY,,,,,STUDIO | 1 |
| ,,,,,,,GALLERY,,WAREHOUSE,,, | 1 |
| ,,,,,,MUSICBAR,,RESTAURANT,,,SHOP, | 1 |
| ,,,CONCERTHALL,,,,,,,,,STUDIO | 1 |
| ,,,CONCERTHALL,,,,GALLERY,,WAREHOUSE,,, | 1 |
| ,,,CONCERTHALL,,,,GALLERY,,WAREHOUSE,,,STUDIO | 1 |
| ,,,CONCERTHALL,,,MUSICBAR,,,WAREHOUSE,,, | 1 |
| ,,,CONCERTHALL,,,MUSICBAR,,RESTAURANT,WAREHOUSE,,, | 1 |
| ,,,CONCERTHALL,,CINEMA,,,,WAREHOUSE,,, | 1 |
| ,,,CONCERTHALL,,CINEMA,MUSICBAR,,RESTAURANT,WAREHOUSE,,,STUDIO | 1 |
| ,,,CONCERTHALL,THEATER,,,,RESTAURANT,WAREHOUSE,,, | 1 |
| ,,,CONCERTHALL,THEATER,CINEMA,,,,WAREHOUSE,,, | 1 |
| ,,OPENAIR,,,,,,,,,, | 1 |
| ,,OPENAIR,,,,,,RESTAURANT,,,, | 1 |
| ,,OPENAIR,,,,,,RESTAURANT,WAREHOUSE,,, | 1 |
| ,,OPENAIR,CONCERTHALL,,,,,,,,,STUDIO | 1 |
| ,,OPENAIR,CONCERTHALL,,,,,,WAREHOUSE,,, | 1 |
| ,,OPENAIR,CONCERTHALL,,,,,,WAREHOUSE,,,STUDIO | 1 |
| ,,OPENAIR,CONCERTHALL,,,,,RESTAURANT,WAREHOUSE,,, | 1 |
| ,,OPENAIR,CONCERTHALL,,,,,RESTAURANT,WAREHOUSE,,,STUDIO | 1 |
| ,,OPENAIR,CONCERTHALL,,,MUSICBAR,,,WAREHOUSE,,SHOP, | 1 |
| ,,OPENAIR,CONCERTHALL,,,MUSICBAR,,RESTAURANT,,,, | 1 |
| ,,OPENAIR,CONCERTHALL,,,MUSICBAR,,RESTAURANT,WAREHOUSE,,, | 1 |
| ,,OPENAIR,CONCERTHALL,,,MUSICBAR,,RESTAURANT,WAREHOUSE,,SHOP,STUDIO | 1 |
| ,DISCO,,,,,,,,WAREHOUSE,,, | 1 |
| ,DISCO,,,,,,,RESTAURANT,WAREHOUSE,,, | 1 |
| ,DISCO,,CONCERTHALL,,,,,,WAREHOUSE,,, | 1 |
| ,DISCO,,CONCERTHALL,,,,,,WAREHOUSE,,,STUDIO | 1 |
| ,DISCO,,CONCERTHALL,,,MUSICBAR,,,,,, | 1 |
| CLUB,,,,,,,,,WAREHOUSE,,, | 1 |
| CLUB,,,CONCERTHALL,,,,,,WAREHOUSE,,, | 1 |
| CLUB,,,CONCERTHALL,,,,,,WAREHOUSE,,,STUDIO | 1 |
| CLUB,,,CONCERTHALL,,,,,RESTAURANT,,,, | 1 |
| CLUB,,,CONCERTHALL,,,,,RESTAURANT,WAREHOUSE,,, | 1 |
| CLUB,,,CONCERTHALL,,,,GALLERY,,,,, | 1 |
| CLUB,,OPENAIR,,,,,,,WAREHOUSE,,, | 1 |
| CLUB,,OPENAIR,,,,MUSICBAR,,,,,, | 1 |
| CLUB,,OPENAIR,CONCERTHALL,,,,,,WAREHOUSE,,, | 1 |
3.2.1. Uses by District
The multi-use phenomenon is fairly widespread, and there isn’t a clear pattern because some of the districts have so few venues, it’s a bit hard to say if there’s a rent-driven trend.
d2_aggregates %>%
filter(is.na(venue_count) == FALSE) %>%
rowwise() %>%
mutate(venueType_sum = sum(c_across(starts_with("venueType_")))) %>%
ungroup() %>%
mutate(mean_uses = venueType_sum/venue_count) %>%
ggplot()+
geom_bar(aes(x = d2_name, y = mean_uses), stat = "identity")+
labs(
title = "Mean space uses by district",
subtitle = "",
x="District",
y="Mean Number of Space Uses",
caption = "Data: CFP, City of Copenhagen")+
plotTheme
d2_aggregates %>%
filter(is.na(venue_count) == FALSE) %>%
rowwise() %>%
mutate(venueType_sum = sum(c_across(starts_with("venueType_")))) %>%
ungroup() %>%
mutate(mean_uses = venueType_sum/venue_count) %>%
ggplot()+
geom_point(aes(y = mean_uses, x = as.numeric(rent_t2)/1000))+
geom_label(aes(y = mean_uses, x = as.numeric(rent_t2)/1000, label = d2_name))+
ylim(0, 5)+
xlim(0, 70)+
geom_smooth(aes(x = as.numeric(rent_t2)/1000, y = mean_uses),
alpha=0.1,
weight = 0.5, color = "red",
se = FALSE,
method = "lm") +
labs(
title = "Mean space uses by district as a function mean property market value",
subtitle = "Mean market values for all structure types, 2022",
x="DKK/sqm - 2022",
y="Mean Number of Space Uses",
caption = "Data: CFP, Finans Danmark, City of Copenhagen")+
plotTheme
3.2.2 Venue Use Summary
Concert hall and warehouse are the most common uses, and are very over-represented in the sample data set. Copenhagen also has a relatively high percentage of open-airs and studio spaces.
The “Quotient” in the plot below represents the ratio at which these uses are represented in the sample city compared to CFP cities Tokyo, Stockholm, Montreal, Sydney and Rotterdam - the cities where we have been using our current protocol to measure use.
main_venue_data %>%
filter(city %in% c("Copenhagen" )) %>%
select(contains("venueType")) %>%
pivot_longer(
cols = starts_with("venueType_"),
names_to = "venueType",
values_to = "count") %>%
group_by(venueType)%>%
summarize(n = sum(count)) %>%
mutate(local_pct = round(100*(n/nrow(main_venue_data %>%
filter(city %in% c("Copenhagen" )))), digits = 2)) %>%
cbind(., main_venue_data %>%
filter(city %in% c("Tokyo", "Stockholm", "Montreal", "Sydney", "Rotterdam")) %>%
select(contains("venueType")) %>%
pivot_longer(
cols = starts_with("venueType_"),
names_to = "venueType",
values_to = "count") %>%
group_by(venueType)%>%
summarize(n = sum(count, na.rm = TRUE)) %>%
mutate(cfp_pct = round(100*(n/nrow(main_venue_data %>%
filter(city %in% c("Tokyo", "Stockholm", "Montreal", "Sydney", "Rotterdam")))), digits = 2)) %>%
select(cfp_pct)) %>%
mutate(quotient = local_pct / cfp_pct)## venueType n local_pct cfp_pct quotient
## 1 venueType_arena 4 3.70 1.42 2.6056338
## 2 venueType_cinema 3 2.78 0.79 3.5189873
## 3 venueType_club 17 15.74 15.80 0.9962025
## 4 venueType_concertHall 58 53.70 29.23 1.8371536
## 5 venueType_disco 10 9.26 7.50 1.2346667
## 6 venueType_gallery 7 6.48 3.40 1.9058824
## 7 venueType_musicBar 22 20.37 38.63 0.5273104
## 8 venueType_openAir 20 18.52 3.71 4.9919137
## 9 venueType_restaurant 26 24.07 30.17 0.7978124
## 10 venueType_shop 5 4.63 1.58 2.9303797
## 11 venueType_studio 22 20.37 3.00 6.7900000
## 12 venueType_theater 4 3.70 6.64 0.5572289
## 13 venueType_warehouse 53 49.07 10.90 4.50183493.3 Multi-Usage City Comparison
Copenhagen has a very large number of multi-use spaces. On average, spaces are used for 2 different programming uses.
main_venue_data %>%
filter(city != "",
city %in% c("Tokyo", "Stockholm", "Montreal", "Sydney", "Rotterdam", "Copenhagen")) %>%
mutate(number_of_uses = rowSums(across(venueType_disco:venueType_studio)))%>%
group_by(number_of_uses, city) %>%
tally() %>%
group_by(city) %>%
mutate(pct = round(100*(n/sum(n)), digits = 2)) %>%
ggplot()+
geom_bar(aes(y = pct, x = number_of_uses, fill = city),
stat = "identity", alpha = 0.6) +
scale_fill_manual(values = c(CityPalette[8], CityPalette[5] , CityPalette[7], CityPalette[3], CityPalette[6], CityPalette[4]))+
theme(legend.direction = "horizontal", legend.position = "bottom")+
facet_wrap(~city)+
labs(
title = "Number of programming or types of use in creative spaces",
subtitle = "Subtitle",
x="Number of Space Usages",
y="Percentage of Venues",
fill = "CFP City",
caption = "Data: CFP")+
plotTheme
main_venue_data %>%
filter(city != "",
city %in% c("Tokyo", "Stockholm", "Montreal", "Sydney", "Rotterdam", "Copenhagen")) %>%
mutate(number_of_uses = rowSums(across(venueType_disco:venueType_studio)))%>%
group_by(city) %>%
summarize(mean_num_uses = mean(number_of_uses),
median_num_uses = median(number_of_uses)) %>%
group_by(city) %>%
kable() %>%
kable_styling()| city | mean_num_uses | median_num_uses |
|---|---|---|
| Copenhagen | 2.166667 | 2 |
| Montreal | 1.756458 | 2 |
| Rotterdam | 2.380282 | 2 |
| Stockholm | 1.088235 | 1 |
| Sydney | 1.630705 | 2 |
| Tokyo | 1.006885 | 1 |
4 Closures
The data on closures that was part of the CFP during and right after COVID is no longer part of the analysis.
Sources: CFP
4.1 Closures by Programming
4.2 Closures Map
5 Districts
Sources: CFP, Centraal Bureau voor de Statistiek
5.0. Dynamic map of districts
Hover over the shapes to see a pop-up icon with some key information about the districts.
Information appended “t_2” corresponds to 2022 data, and “t_1” to 2017.
d2_aggregates %>%
mutate(venue_kmsq = venue_count / area_km2) %>%
select(mean_creative_output, mean_experimentation, mean_community_focus, mean_promotion, d2_id, d2_name,
venue_count, venue_kmsq, station_count, transit_stations_per_km2, area_km2, pop_per_km2_t2, rent_t2, income_t2, change_rent) %>%
mapview(., zcol = "venue_count")5.1 Number of venues per district map
Venues most concentrated in Indre By, but there are notable numbers of venues in 3-4 other districts.
These maps will need to be styled in illustrator to deal with labels.
ggplot()+
geom_sf(data = d2_aggregates, fill = 'transparent',
color = 'grey')+
geom_sf(data = d2_aggregates %>%
filter(is.na(venue_count) == FALSE),
aes(fill = venue_count),
color = 'grey', alpha = 0.6)+
scale_fill_viridis('Venue Count', direction = -1, alpha = 0.8)+
geom_text(data = d2_aggregates %>%
filter(venue_count != 0) %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x = lon, y = lat, label = d2_name),
color = "black", size = 3)+
labs(
title = "Venues Count by District",
caption = "Data: CFP, City of Copenhagen")+
mapTheme
5.2 Number of venues per district chart
d2_aggregates %>%
filter(venue_count != 0) %>%
ggplot()+
geom_bar(aes(x = reorder(toupper(d2_name), venue_count), y = venue_count),
stat = "identity", fill = CityPalette[8], alpha = 0.6, size = 4 )+
labs(
title = "Venues by District",
#subtitle = "NUMBER districts have no reported venues",
x="",
y="Total Venues",
#fill = "CFP City",
caption = "Data: CFP, City of Copenhagen")+
coord_flip()+
plotTheme
#ggsave("rotterdam_images/venues_district_bar.pdf", width = 11, height = 8, units = "in")5.3 Venue Density by District - Symbology
The central districts of Indre By and Norrebro have the highest venue density.
temp <- d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2) %>%
filter(is.na(venue_count) == FALSE)
ggplot()+
geom_sf(data = d2_aggregates, fill = 'transparent',
color = 'grey')+
geom_sf(data = d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2) %>%
filter(is.na(venue_count) == FALSE),
aes(fill = venue_dens),
color = 'grey')+
scale_fill_viridis('Venue Density\n Venues/km^2', direction = -1,
#limits=c(0,32),
#breaks=c(0,32),
alpha = 0.6,
#labels=c("Minimum","Maximum"),
breaks = c(quantile(temp$venue_dens, probs = seq(0, 1, length.out = 5))),
labels = scales::number_format(scale = 1))+
geom_text(data = d2_aggregates %>%
filter(venue_count !=0) %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x = lon, y = lat, label = d2_name),
color = "black", size = 3)+
labs(
title = "Venues Density by District",
caption = "Data: CFP, City of Copenhagen")+
mapTheme
#ggsave("rotterdam_images/venues_district_chloropleth.pdf", width = 11, height = 8, units = "in")5.4 Venue Density - Heatmap
# Heat map
# Make a fishnet
fishnet <- st_make_grid(d2_aggregates %>%
st_transform(crs = 3035),
cellsize = 500) %>% #size in meters
st_sf() %>%
st_transform(crs = 4326)
# If this fails, run the following line of code:
# sf_use_s2(FALSE)
fishnet <- fishnet[d2_aggregates %>%
st_transform(crs = 4326),] %>%
mutate(uniqueID = rownames(.)) %>%
select(uniqueID) %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]]))
# Join the fishet to the points
fishnet <- st_join(fishnet,
main_venue_data %>%
filter(city == "Copenhagen",
is.na(uid) == FALSE,
is.na(x)== FALSE,
is.na(y)== FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326),
join = st_intersects,
left = TRUE) %>%
select(uniqueID, uid) %>%
group_by(uniqueID) %>%
summarise(n_venues = n_distinct(uid, na.rm = TRUE)) %>%
#mutate(n = ifelse(n == 1, 0, n)) %>%
#rename(n_venues = n) %>%
as.data.frame() %>%
select(-geometry) %>%
left_join(fishnet, .)# Make sure you set the labels here to reflect the actual max density by cell
max(fishnet$n_venues)## [1] 6ggplot()+
geom_sf(data = d2_aggregates,
fill = '#f0f0f0',
color = 'white')+
geom_sf(data = fishnet %>%
filter(n_venues >0),
aes(fill = n_venues),
color = "transparent",
alpha = 0.4)+
#geom_text(data = d1_aggregates %>%
# filter(city_en == "Montreal",
# venue_count !=0) %>%
# mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
# lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
# aes(x=lon, y=lat, label=d1_name), size = 3, alpha = 0.4)+
scale_fill_viridis('VENUES/\nkm^2', direction = -1,
limits=c(1,max(fishnet$n_venues)), breaks=c(1, max(fishnet$n_venues)),
labels=c("1",max(fishnet$n_venues)))+
labs(
title = "VENUE DENSITY",
subtitle = "",
caption = "Data: CFP, City of Copenhagen")+
mapTheme
#ggsave("rotterdam_images/venues_district_heatmap.pdf", width = 11, height = 8, units = "in")5.5 Venue Density with Experimental Content
The districts in the nearby north and west of Indre By have the highest experimental content ratings.
ggplot()+
geom_sf(data = d2_aggregates,
fill = '#f0f0f0',
color = 'white')+
geom_point(data = d2_aggregates %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x=lon, y=lat, size = venue_count,
color = mean_experimentation),
alpha = 0.4) +
scale_color_viridis('Mean Experimental\nLikelihood Score', direction = -1,
limits = c(1, 4),
breaks = c(1,2, 3, 4),
labels = c("Not At All", "Not Very", "Somewhat", "Extremely"))+
scale_size_area(name="", max_size = 25, guide = 'none') +
#guides(size=guide_legend("Total Venues")) +
geom_text(data = d2_aggregates %>%
filter(venue_count != 0) %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x=lon, y=lat, label=d2_name), size = 3, alpha = 0.65)+
labs(
title = "Creative Footprint Venue",
subtitle = "Icon sizes represent total number of venues per district.",
caption = "Data: CFP, City of Copenhagen")+
mapTheme
5.6 Districts and Experimental Content
# Top wards for experimental content
ggplot(data = d2_aggregates %>%
filter(is.na(venue_count) == FALSE) %>%
as.data.frame() %>%
mutate(venue_text = str_c(as.character(venue_count), " venues")) %>%
select(venue_text, venue_count, mean_experimentation, d2_name) %>%
arrange(-mean_experimentation) %>%
top_n(10))+
geom_bar(aes(x = reorder(toupper(d2_name), mean_experimentation),
y=mean_experimentation),
stat = "identity", fill = CityPalette[8], width = 0.3,
alpha = 0.5)+
scale_y_continuous(breaks = 1:4,
labels = c("Not At All", "Not Too", "Somewhat", "Very"),
expand = c(0, 1))+
geom_text(aes(label = toupper(venue_text), x = toupper(d2_name),
y= mean_experimentation / 2), alpha = 0.6, size = 4 )+
labs(
title = "DISTRICTS WITH HIGHEST EXPERIMENTAL CONTENT SCORES",
#subtitle = "Labels indicate total number of districts in that ward",
caption = "Data: CFP, City of Copenhagen")+
ylab("Mean Likelihood of Experimental Program")+
xlab("")+
coord_flip()+
plotTheme
ggplot()+
geom_sf(data = d2_aggregates, fill = 'transparent',
color = 'grey')+
geom_sf(data = d2_aggregates %>%
filter(is.na(venue_count) == FALSE),
aes(fill = mean_experimentation),
color = 'grey', alpha = 0.6)+
scale_fill_viridis('Mean Experimental\nLikelihood Score', direction = -1,
limits = c(1, 4),
breaks = c(1,2, 3, 4),
labels = c("Not At All", "Not Very", "Somewhat", "Extremely"))+
geom_text(data = d2_aggregates %>%
filter(venue_count != 0) %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x = lon, y = lat, label = d2_name),
color = "black", size = 3)+
labs(
title = "Experimental Content Scores By District",
subtitle = "",
caption = "Data: CFP, City of Copenhagen")+
mapTheme
#ggsave("rotterdam_images/experimental_districts_chloropleth.pdf", width = 11, height = 8, units = "in")5.7 Venue Density with Community Focused Content
The average scores in the Centrum are not substantially poorer than elsewhere, but they are lower, on average. When we do the clustering analysis, we see that there is some heterogenaeity in the Centrum.
ggplot()+
geom_sf(data = d2_aggregates,
fill = '#f0f0f0',
color = 'white')+
geom_point(data = d2_aggregates %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x=lon, y=lat, size = venue_count,
color = mean_community_focus),
alpha = 0.4) +
scale_color_viridis('Mean Community Focus\nLikelihood Score', direction = -1,
limits = c(1, 4),
breaks = c(1,2, 3, 4),
labels = c("Not At All", "Not Very", "Somewhat", "Extremely"))+
scale_size_area(name="", max_size = 25, guide = 'none') +
#guides(size=guide_legend("Total Venues")) +
geom_text(data = d2_aggregates %>%
filter(venue_count != 0) %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x=lon, y=lat, label=d2_name), size = 3, alpha = 0.65)+
labs(
title = "Creative Footprint Venues",
subtitle = "Icons represent total number of venues per district",
caption = "Data: CFP, City of Copenhagen")+
mapTheme
ggplot()+
geom_sf(data = d2_aggregates, fill = 'transparent',
color = 'grey')+
geom_sf(data = d2_aggregates %>%
filter(is.na(venue_count) == FALSE),
aes(fill = mean_community_focus),
color = 'grey', alpha = 0.6)+
scale_fill_viridis('Mean Community Focus\nLikelihood Score', direction = -1,
limits = c(1, 4),
breaks = c(1,2, 3, 4),
labels = c("Not At All", "Not Very", "Somewhat", "Extremely"))+
geom_text(data = d2_aggregates %>%
filter(venue_count != 0) %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x = lon, y = lat, label = d2_name),
color = "black", size = 3)+
labs(
title = "Community Focus Scores By District",
subtitle = "",
caption = "Data: CFP, City of Copenhagen")+
mapTheme
#ggsave("rotterdam_images/community_districts_chloropleth.pdf", width = 11, height = 8, units = "in")# Top wards for community content
ggplot(data = d2_aggregates %>%
filter(is.na(mean_community_focus) == FALSE) %>%
as.data.frame() %>%
mutate(venue_text = str_c(as.character(venue_count), " venues")) %>%
select(venue_text, venue_count, mean_community_focus, d2_name) %>%
arrange(-mean_community_focus) %>%
top_n(10))+
geom_bar(aes(x = reorder(toupper(d2_name), mean_community_focus),
y=mean_community_focus),
stat = "identity", fill = CityPalette[6], width = 0.3,
alpha = 0.8)+
scale_y_continuous(breaks = 1:4,
labels = c("Not At All", "Not Too", "Somewhat", "Very"),
expand = c(0, 1))+
geom_text(aes(label = toupper(venue_text), x = toupper(d2_name),
y= mean_community_focus / 2), alpha = 0.6, size = 4 )+
labs(
title = "Districts with highest community likelihood scores",
#subtitle = "Labels indicate total number of districts in that ward",
caption = "Data: CFP, City of Copenhagen")+
ylab("Mean Likelihood of Community-Oriented Program")+
xlab("")+
coord_flip()+
plotTheme
5.8 Venue Density with Creative Content
There are notably high creativity scores all across the city, including in Indre By. Norrebro jumps off the chart once again.
ggplot()+
geom_sf(data = d2_aggregates,
fill = '#f0f0f0',
color = 'white')+
geom_point(data = d2_aggregates %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x=lon, y=lat, size = venue_count,
color = mean_creative_output),
alpha = 0.4) +
scale_color_viridis('Mean Creative Content\nLikelihood Score', direction = -1,
limits = c(1, 4),
breaks = c(1,2, 3, 4),
labels = c("Not At All", "Not Very", "Somewhat", "Extremely"))+
scale_size_area(name="", max_size = 25, guide = 'none') +
#guides(size=guide_legend("Total Venues")) +
geom_text(data = d2_aggregates %>%
filter(venue_count != 0) %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x=lon, y=lat, label=d2_name), size = 3, alpha = 0.65)+
labs(
title = "Creative Footprint Venues",
subtitle = "Icons represent total number of venues per district.",
caption = "Data: CFP, City of Copenhagen")+
mapTheme
# Top for community content
ggplot(data = d2_aggregates %>%
filter(is.na(mean_creative_output) == FALSE) %>%
as.data.frame() %>%
mutate(venue_text = str_c(as.character(venue_count), " venues")) %>%
select(venue_text, venue_count, mean_creative_output, d2_name) %>%
arrange(-mean_creative_output) %>%
top_n(10))+
geom_bar(aes(x = reorder(toupper(d2_name), mean_creative_output),
y=mean_creative_output),
stat = "identity", fill = CityPalette[6], width = 0.3,
alpha = 0.8)+
scale_y_continuous(breaks = 1:4,
labels = c("Not At All", "Not Too", "Somewhat", "Very"),
expand = c(0, 1))+
geom_text(aes(label = toupper(venue_text), x = toupper(d2_name),
y= mean_creative_output / 2), alpha = 0.6, size = 4 )+
labs(
title = "Districts with highest creative likelihood scores",
#subtitle = "Labels indicate total number of districts in that ward",
caption = "Data: CFP, City of Copenhagen")+
ylab("Mean Creative Program Likelihood Rating")+
xlab("")+
coord_flip()+
plotTheme
6 Urban Variables
Sources: CFP, City of Copenhagen, Finans Danmark
6.1 Urban Variable Maps
6.1.1. Rents
Rents are higher in the central areas - with Indre By and neighboring districts reporting the highest median rents.
ggplot()+
geom_sf(data = d2_aggregates, fill = 'transparent',
color = 'grey')+
geom_sf(data = d2_aggregates %>%
filter(is.na(rent_t2) == FALSE),
aes(fill = as.numeric(rent_t2)/1000),
color = 'grey')+
scale_fill_viridis('DKK / sqm', direction = -1, alpha = 0.8)+
geom_text(data = d2_aggregates %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x = lon, y = lat, label = d2_name),
color = "black", size = 3)+
labs(
title = "Mean reported property prices",
subtitle = "DKK/sqm, for all types of dwellings, 2022.",
caption = "Data: Finans Danmark, City of Copenhagen")+
mapTheme
6.1.2. Change in Property Values
Property values are appreciating slighlty faster in the eastern districts. This growth is not nearly as dramatic in Copenhagen than in other recent CFP cities such as Rotterdam.
Based on 2017-2022 figures, adjusted to 2022 rates using the following CPI adjustment:
https://www.global-rates.com/en/calculations/inflation-calculator/#amount
ggplot()+
geom_sf(data = d2_aggregates, fill = 'transparent',
color = 'grey')+
geom_sf(data = d2_aggregates %>%
filter(is.na(change_rent) == FALSE),
aes(fill = 100*(as.numeric(change_rent) / (.93*as.numeric(rent_t1)))),
color = 'grey')+
scale_fill_viridis('Percentage Change', direction = -1, alpha = 0.8)+
geom_text(data = d2_aggregates %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x = lon, y = lat, label = d2_name),
color = "black", size = 3)+
labs(
title = "Change in mean reported property prices",
subtitle = "For all types of dwellings, 2017-2022, inflation adjusted",
caption = "Data: Finans Danmark, City of Copenhagen")+
mapTheme
6.1.3. Income
It’s notable that income is somewhat lower in some venue rich areas (e.g. Norrebro) and higher in others (Indre By) - there are venue clusters everywhere!
ggplot()+
geom_sf(data = d2_aggregates, fill = 'transparent',
color = 'grey')+
geom_sf(data = d2_aggregates %>%
filter(is.na(income_t2) == FALSE),
aes(fill = as.numeric(income_t2)),
color = 'grey')+
scale_fill_viridis('Income', direction = -1, alpha = 0.8)+
geom_text(data = d2_aggregates %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x = lon, y = lat, label = d2_name),
color = "black", size = 3)+
labs(
title = "Mean Personal Income",
subtitle = "Personal income in total (ex. imputed rent and before deductions of interest expenses), DKK, all persons 14+, 2022",
caption = "Data: City of Copenhagen")+
mapTheme
6.1.4. Change in Income
There is an inflation adjusted rise in overall incomes in Copenhagen. Property values are lagging somewhat behind incomes as an indicator of change.
ggplot()+
geom_sf(data = d2_aggregates, fill = 'transparent',
color = 'grey')+
geom_sf(data = d2_aggregates %>%
filter(is.na(change_income) == FALSE),
aes(fill = 100*(as.numeric(change_income) / (.92*as.numeric(income_t1)))),
color = 'grey')+
scale_fill_viridis('Percentage', direction = -1, alpha = 0.8)+
geom_text(data = d2_aggregates %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x = lon, y = lat, label = d2_name),
color = "black", size = 3)+
labs(
title = "Percentage Change in Mean Income, 2017-2022",
subtitle = "Adjusted for inflation",
caption = "Data: City of Copenhagen")+
mapTheme
6.2. Transit Density
There is fairly widespread fixed transit access across the city - most concentrated in areas surrounding Frederiksberg. Based on testimony in focus groups, it seems clear that multi-modal transport is more both important and highly valued in Copenhagen.
Transit data includes only locations of fixed Metro and S train stops, not busses, trams etc.,
Source: City of Copenhagen, OpenData DK, Rezk (2020)
ggplot()+
geom_sf(data = d2_aggregates, fill = 'transparent',
color = 'grey')+
geom_sf(data = d2_aggregates %>%
filter(is.na(transit_stations_per_km2) == FALSE),
aes(fill = transit_stations_per_km2),
color = 'grey')+
scale_fill_viridis('Stations/km^2', direction = -1, alpha = 0.8)+
geom_text(data = d2_aggregates %>%
mutate(lon=map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat=map_dbl(geometry, ~st_centroid(.x)[[2]])),
aes(x = lon, y = lat, label = d2_name),
color = "black", size = 3)+
labs(
title = "Transit Density",
subtitle = "Fixed Rail Stations / km^2 - Metro and S stations only",
caption = "Data: City of Copenhagen, OpenData DK, Rezk (2020)")+
mapTheme
6.2 Venue density as a function of transit density
General trends across CFP cities show a positive relationship between venue density and fixed transit density. Even in auto-oriented cities Sydney and Montreal, this trend generally holds (First plot). Rotterdam has a strong correlation - as the Centrum has far and away the highest transit density and the highest venue density in the city. (Third plot).
ggplot(d1_aggregates %>%
as.data.frame() %>%
filter(city_en %in% c("Tokyo", "Berlin", "Montreal", "Stockholm")) %>%
filter(! d1_name %in% remove_arrondissements) %>%
mutate(venue_dens = venue_count / area_km2,
venue_dens = ifelse(venue_dens == 0, 0, venue_dens),
city = case_when(city_en == "Berlin" ~ "1. BERLIN, 2017",
city_en == "Tokyo" ~ "3. TOKYO, 2019",
city_en == "Stockholm" ~ "4. STOCKHOLM, 2021",
city_en == "Montreal" ~ "5. MONTREAL, 2022")) %>%
select(city, venue_dens, transit_stations_per_km2) %>%
rbind(., d2_aggregates_all %>%
as.data.frame() %>%
filter(city_en %in% c("Sydney", "New York", "Rotterdam")) %>%
mutate(venue_dens = venue_count / area_km2,
venue_dens = ifelse(venue_dens == 0, 0, venue_dens),
city = case_when(city_en == "New York" ~ "2. NEW YORK, 2018",
city_en == "Sydney" ~ "6. SYDNEY, 2023",
city_en == "Rotterdam" ~ "7. ROTTERDAM, 2024")) %>%
select(city, venue_dens, transit_stations_per_km2)) %>%
rbind(., d2_aggregates %>%
as.data.frame() %>%
mutate(venue_dens = venue_count / area_km2,
venue_dens = ifelse(venue_dens == 0, 0, venue_dens),
city = "8. COPENHAGEN, 2024") %>%
select(city, venue_dens, transit_stations_per_km2))) +
geom_point(aes(x = transit_stations_per_km2, y = venue_dens, color = city),
size = 1, alpha = 0.6)+
geom_hline(aes(yintercept = median(d1_aggregates$venue_count / d1_aggregates$area_km, na.rm = TRUE)))+
geom_vline(aes(xintercept = median(d1_aggregates$transit_stations_per_km2, na.rm = TRUE)))+
scale_color_manual(values = c(CityPalette[1], CityPalette[2], CityPalette[3], CityPalette[4], CityPalette[5], CityPalette[6], CityPalette[7], CityPalette[8]))+
# scale_x_continuous(breaks = c(0, median(d1_aggregates$transit_stations_per_km2, na.rm = TRUE),
# 2.5, max(d1_aggregates$transit_stations_per_km2, na.rm = TRUE)),
# labels = c("NONE", "MEDIAN", "HIGH", "MAXIMUM"))+
# scale_y_continuous(breaks = c(0, median(d1_aggregates$venue_count / d1_aggregates$area_km, na.rm = TRUE),
# 5.5, max(d1_aggregates$venue_count / d1_aggregates$area_km, na.rm = TRUE)),
# labels = c("NONE", "MEDIAN", "HIGH", "MAXIMUM"))+
geom_smooth(aes(x = transit_stations_per_km2, y = venue_dens),
alpha=0.1,
weight = 0.5, color = "light grey", linetype="dashed",
se = FALSE,
method = "lm") +
labs(
title = "VENUE DENSITY AND TRANSIT DENSITY GO TOGETHER",
subtitle = "EACH POINT REPRESENTS NTA (NY), WARD (TOKYO), DISTRICT (SWE), \nARRONDISSEMENT (MON), SA2 (SYDNEY), GEBIEDEN (ROT), DISTRICT (COP), OR BEZIRK (BER)\nDOTTED LINE REPRESENTS BEST FIT TREND.",
color = "CFP CITY",
caption = "Data: CFP, Centraal Bureau voor de Statistiek, Montreal Open Data, estat.go.jp, US Census Bureau, City of New York, \nGeodaten aus Deutchland, daten.berlin.de, statistikdatabasen.scb.se\nOpen Data Montreal, Statistics Canada, New South Wales Open Data, Australian Bureau of Statistics, Centraal Bureau voor de Statistiek, City of Copenhagen, Resk (2020)")+
ylab("VENUE DENSITY")+
xlab("RAIL DENSITY")+
plotTheme
d2_aggregates %>%
mutate(has_venues = ifelse(is.na(venue_count) == TRUE, "No Venues", "Has Venues")) %>%
as.data.frame() %>%
ggplot()+
geom_histogram(aes(transit_stations_per_km2),
binwidth = .5, alpha = 0.6, fill = CityPalette[6])+
facet_wrap(~has_venues)+
labs(
title = "Fixed Transit Station Density by District",
subtitle = "Areas with venues don't necessarily have access to transit",
caption = "Data: Centraal Bureau voor de Statistiek, ArcGIS Online Feature")+
ylab("n districts")+
xlab("Fixed Rail Transit Stops per km by District")+
plotThemed2_aggregates %>%
mutate(venue_dens = venue_count / area_km2) %>%
ggplot()+
geom_point(aes(x = transit_stations_per_km2, y = venue_dens))+
geom_text(aes(x = transit_stations_per_km2, y = venue_dens, label = d2_name),
nudge_x = .1, nudge_y = 0.1, alpha = 0.2, size = 3) +
geom_smooth(aes(x = transit_stations_per_km2, y = venue_dens),
alpha=0.1,
weight = 0.5, color = "light grey", linetype="dashed",
se = FALSE,
method = "lm")+
labs(
title = "Venue Density by Fixed Transit Station Density by District",
subtitle = "",
caption = "Data: City of Copenhagen, OpenData DK, Resk (2020)")+
ylab("Venue Density")+
xlab("Rail Density")+
plotTheme
6.2.2 Relationships Between Transit and Rents
d2_aggregates %>%
ggplot()+
geom_point(aes(x = transit_stations_per_km2, y = as.numeric(rent_t2)/1000),
size = 2, alpha = 0.6, fill = CityPalette[8])+
geom_smooth(aes(x = transit_stations_per_km2, y = as.numeric(rent_t2)/1000),
alpha=0.1,
weight = 0.5, color = "light grey", linetype="dashed",
se = FALSE,
method = "lm")+
geom_text(aes(x = transit_stations_per_km2, y = as.numeric(rent_t2)/1000, label = d2_name),
nudge_x = .1, nudge_y = 0.1, alpha = 0.2, size = 3) +
xlim(0,1.5)+
ylim(0,70)+
labs(
title = "Relationship Between Housing Prices and Density",
subtitle = "Each point represents one of the districts the study area,\n Values in mean listed value, DKK/sqm, 2022 for all structure types",
caption = "Data: City of Copenhagen, Finans Danmark, OpenData DK, Resk (2020)")+
ylab("Mean District Sales Prices(DKK/sqm)")+
xlab("Fixed Transit Stations / km2")+
plotTheme
6.3 Urban Variables and Venue Density
In this section we present correlations between urban variables and venue density.
The tables of Pearson’s R (or Pearson’s Rho) are measures of statistical correlation. 1 is a strong positive correlation, -1 is a strong negative correlation, and 0 is no correlation.
d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2,
rent_t2 = as.numeric(rent_t2),
income_t2 = as.numeric(income_t2)) %>%
as.data.frame() %>%
select(venue_dens, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-venue_dens, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Median Weekly Household Income",
variable == "rent_t2" ~ "Median Weekly Rent")) %>%
ggplot()+
geom_point(aes(y = venue_dens, x = value), color = CityPalette[6], alpha = 0.6)+
facet_wrap(~variable, scales = "free")+
geom_line(stat = "smooth", method='lm',
aes(y = venue_dens, x = value),
se = FALSE,
linetype = "dashed", alpha = 0.5)+
labs(
title = "Venue Density as a Function of Urban Variables",
subtitle = "Each point represents one of the districts with venues in the survey",
caption = "Data: CFP, City of Copenhagen, OpenData DK, Finans Danmark, Resk (2020)")+
ylab("Venues / km2")+
xlab("")+
plotTheme
d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2,
rent_t2 = as.numeric(rent_t2),
income_t2 = as.numeric(income_t2)) %>%
as.data.frame() %>%
select(venue_dens, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-venue_dens, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Median Weekly Household Income",
variable == "rent_t2" ~ "Median Weekly Rent")) %>%
filter(is.na(venue_dens) == FALSE) %>%
group_by(variable) %>%
summarize(pearsons_r = cor(as.numeric(value), venue_dens, method = "pearson")) %>%
kable() %>%
kable_styling()| variable | pearsons_r |
|---|---|
| Mean Pedestrian Frequency (Max - 3) | 0.2461591 |
| Median Weekly Household Income | 0.3334928 |
| Median Weekly Rent | 0.7294358 |
| Percentage of Young Adults | 0.5152338 |
| Population / km2 | 0.6312582 |
| Transit Stations / km2 | 0.4714336 |
Copenhagen has similar economic geography to other European, mono-centric cities where transit density and incomes density have a positive correlation.
Some data are missing for Berlin and Tokyo…
It’s worth noting that Montreal and Sydney have auto-oriented urban form, and other cities in the CFP sample have transit-oriented form and land markets.
d1_aggregates %>%
filter(city %in% c("Montreal", "Stockholm")) %>%
mutate(transit_stations_per_km2 = ifelse(is.na(transit_stations_per_km2) == TRUE, 0, transit_stations_per_km2)) %>%
as.data.frame() %>%
select(income_t2, transit_stations_per_km2, city) %>%
rename(city_en = city) %>%
rbind(.,
d2_aggregates_all %>%
filter(city_en %in% c("Sydney", "New York", "Rotterdam")) %>%
mutate(transit_stations_per_km2 = ifelse(is.na(transit_stations_per_km2) == TRUE,
0, transit_stations_per_km2))%>%
as.data.frame() %>%
select(income_t2, transit_stations_per_km2, city_en) %>%
rename(city_en = city_en)) %>%
rbind(., d2_aggregates %>%
mutate(transit_stations_per_km2 = ifelse(is.na(transit_stations_per_km2) == TRUE, 0,
transit_stations_per_km2)) %>%
as.data.frame() %>%
select(income_t2, transit_stations_per_km2, city_en) %>%
rename(city_en = city_en) ) %>%
ggplot()+
geom_point(aes(y = as.numeric(income_t2), x = transit_stations_per_km2),
alpha = 0.6, color = CityPalette[6]) +
geom_line(stat = "smooth", method='lm',
aes(y = as.numeric(income_t2), x = transit_stations_per_km2),
se = FALSE,
linetype = "dashed", alpha = 0.5)+
facet_wrap(~city_en, scales = "free")+
labs(
title = "Area Income as a Function of Transit Density",
subtitle = "Each point represents a top level administrative district",
caption = "CFP, US Census Bureau, City of New York, \nGeodaten aus Deutchland, daten.berlin.de, statistikdatabasen.scb.se\nOpen Data Montreal, Statistics Canada, New South Wales Open Data, Australian Bureau of Statistics, Centraal Bureau voor de Statistiek, City of Copenhagen, Resk (2020), Finans Danmark, OpenData DK")+
ylab("Average District Income in Local Currency\nTime Periods Vary")+
xlab("Fixed Transit Stops per km2 by District")+
plotTheme
6.4 Urban variables and venue characteristics
Copenhagen’s venues tend to cluster near transit - and so do higher rents and incomes. Meanwhile, program ratings are negatively correlated with rents, incomes, and transit density - there is lower program rating nearer to the center.
6.4.1 Urban variables and Experimentation
Experimentation seems to be inversely related to property values - where there is higher rent, there is lower experimentation.
d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(mean_experimentation, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-mean_experimentation, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Median Weekly Household Income",
variable == "rent_t2" ~ "Median Weekly Rent")) %>%
ggplot()+
geom_point(aes(y = mean_experimentation, x = as.numeric(value)), color = CityPalette[6], alpha = 0.6)+
facet_wrap(~variable, scales = "free")+
geom_line(stat = "smooth", method='lm',
aes(y = mean_experimentation, x = as.numeric(value)),
se = FALSE,
linetype = "dashed", alpha = 0.5)+
labs(
title = "Mean Experimentation Score as a Function of Urban Variables",
subtitle = "Each point represents a district with venues in the survey",
caption = "Data: City of Copenhagen, Resk (2020), Finans Danmark, OpenData DK")+
ylab("Experimentation Score")+
xlab("")+
plotTheme
d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(mean_experimentation, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-mean_experimentation, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Median Weekly Household Income",
variable == "rent_t2" ~ "Median Weekly Rent")) %>%
filter(is.na(mean_experimentation) == FALSE) %>%
group_by(variable) %>%
summarize(pearsons_r = cor(as.numeric(value), mean_experimentation, method = "pearson")) %>%
kable() %>%
kable_styling()| variable | pearsons_r |
|---|---|
| Mean Pedestrian Frequency (Max - 3) | 0.3231595 |
| Median Weekly Household Income | -0.3171473 |
| Median Weekly Rent | -0.2304216 |
| Percentage of Young Adults | -0.3038922 |
| Population / km2 | 0.4557105 |
| Transit Stations / km2 | 0.0838026 |
6.4.2 Urban variables and Creative Output
Creative output is negatively correlated with income and property values.
d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(mean_creative_output, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-mean_creative_output, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Median Weekly Household Income",
variable == "rent_t2" ~ "Median Weekly Rent")) %>%
ggplot()+
geom_point(aes(y = mean_creative_output, x = as.numeric(value)), color = CityPalette[6], alpha = 0.6)+
facet_wrap(~variable, scales = "free")+
geom_line(stat = "smooth", method='lm',
aes(y = mean_creative_output, x = as.numeric(value)),
se = FALSE,
linetype = "dashed", alpha = 0.5)+
labs(
title = "Mean Creative Output Score as a Function of Urban Variables",
subtitle = "Each point represents a district with venues in the survey",
caption = "Data: City of Copenhagen, Resk (2020), Finans Danmark, OpenData DK")+
ylab("Creative Output Score")+
xlab("")+
plotTheme
d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(mean_creative_output, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-mean_creative_output, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Median Weekly Household Income",
variable == "rent_t2" ~ "Median Weekly Rent")) %>%
filter(is.na(mean_creative_output) == FALSE) %>%
group_by(variable) %>%
summarize(pearsons_r = cor(as.numeric(value), mean_creative_output, method = "pearson")) %>%
kable() %>%
kable_styling()| variable | pearsons_r |
|---|---|
| Mean Pedestrian Frequency (Max - 3) | 0.5050916 |
| Median Weekly Household Income | -0.4431312 |
| Median Weekly Rent | -0.4339722 |
| Percentage of Young Adults | -0.2084346 |
| Population / km2 | 0.0515920 |
| Transit Stations / km2 | -0.7357791 |
6.4.3 Urban variables and Promotion
Again, we see negative correlations with this metric and rents/income
d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(mean_promotion, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-mean_promotion, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Median Weekly Household Income",
variable == "rent_t2" ~ "Median Weekly Rent")) %>%
ggplot()+
geom_point(aes(y = mean_promotion, x = as.numeric(value)), color = CityPalette[6], alpha = 0.6)+
facet_wrap(~variable, scales = "free")+
geom_line(stat = "smooth", method='lm',
aes(y = mean_promotion, x = as.numeric(value)),
se = FALSE,
linetype = "dashed", alpha = 0.5)+
labs(
title = "Mean Promotion Score as a Function of Urban Variables",
subtitle = "Each point represents an district with venues in the survey",
caption = "Data: City of Copenhagen, Resk (2020), Finans Danmark, OpenData DK")+
ylab("Mean Promotion Score")+
xlab("")+
plotTheme
d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(mean_promotion, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-mean_promotion, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Median Weekly Household Income",
variable == "rent_t2" ~ "Median Weekly Rent")) %>%
filter(is.na(mean_promotion) == FALSE) %>%
group_by(variable) %>%
summarize(pearsons_r = cor(as.numeric(value), mean_promotion, method = "pearson")) %>%
kable() %>%
kable_styling()| variable | pearsons_r |
|---|---|
| Mean Pedestrian Frequency (Max - 3) | 0.4002502 |
| Median Weekly Household Income | -0.3360219 |
| Median Weekly Rent | -0.4988834 |
| Percentage of Young Adults | -0.4492016 |
| Population / km2 | -0.1317831 |
| Transit Stations / km2 | -0.7351295 |
6.4.4 Urban variables and Community Focus
Community focus is negatively correlated with rents and income.
d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(mean_community_focus, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-mean_community_focus, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Median Weekly Household Income",
variable == "rent_t2" ~ "Median Weekly Rent")) %>%
ggplot()+
geom_point(aes(y = mean_community_focus, x = as.numeric(value)), color = CityPalette[6], alpha = 0.6)+
facet_wrap(~variable, scales = "free")+
geom_line(stat = "smooth", method='lm',
aes(y = mean_community_focus, x = as.numeric(value)),
se = FALSE,
linetype = "dashed", alpha = 0.5)+
labs(
title = "Mean Community Focus Score as a Function of Urban Variables",
subtitle = "Each point represents a district with venues in the survey",
caption = "Data: City of Copenhagen, Resk (2020), Finans Danmark, OpenData DK")+
ylab("Community Focus Score")+
xlab("")+
plotTheme
d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(mean_community_focus, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-mean_community_focus, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Median Weekly Household Income",
variable == "rent_t2" ~ "Median Weekly Rent")) %>%
filter(is.na(mean_community_focus) == FALSE) %>%
group_by(variable) %>%
summarize(pearsons_r = cor(as.numeric(value), mean_community_focus, method = "pearson")) %>%
kable() %>%
kable_styling()| variable | pearsons_r |
|---|---|
| Mean Pedestrian Frequency (Max - 3) | 0.4364714 |
| Median Weekly Household Income | -0.4954926 |
| Median Weekly Rent | -0.3362898 |
| Percentage of Young Adults | 0.0725070 |
| Population / km2 | 0.3825478 |
| Transit Stations / km2 | -0.0087043 |
6.4.5. All Variables Pearson Table
What is the relationship between the following urban factors and the programming or venue density characteristics of Districts?
Over .6 - strong .2 - .6 - Correlated Under .2 - No correlation
Sources: City of Copenhagen, Resk (2020), Finans Danmark, OpenData DK
d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2,
rent_t2 = as.numeric(rent_t2),
income_t2 = as.numeric(income_t2)) %>%
as.data.frame() %>%
select(venue_dens, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-venue_dens, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Income",
variable == "rent_t2" ~ "Rent")) %>%
filter(is.na(venue_dens) == FALSE) %>%
group_by(variable) %>%
summarize(pearsons_r = cor(as.numeric(value), venue_dens, method = "pearson")) %>%
mutate(pearsons_r = case_when(pearsons_r >= 0.6 ~ "Positive (Strong)",
pearsons_r < 0.6 & pearsons_r >= 0.2 ~ "Positive",
pearsons_r < 0.2 & pearsons_r > -0.2 ~ "No Correlation",
pearsons_r <= -0.6 ~ "Negative (Strong)",
pearsons_r > -0.6 & pearsons_r <= -0.2 ~ "Negative")) %>%
spread(-pearsons_r, -variable) %>%
mutate(Variable = "Venue Density")%>%
rbind(., d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(mean_experimentation, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-mean_experimentation, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Income",
variable == "rent_t2" ~ "Rent")) %>%
filter(is.na(mean_experimentation) == FALSE) %>%
group_by(variable) %>%
summarize(pearsons_r = cor(as.numeric(value), mean_experimentation, method = "pearson")) %>%
mutate(pearsons_r = case_when(pearsons_r >= 0.6 ~ "Positive (Strong)",
pearsons_r < 0.6 & pearsons_r >= 0.2 ~ "Positive",
pearsons_r < 0.2 & pearsons_r > -0.2 ~ "No Correlation",
pearsons_r <= -0.6 ~ "Negative (Strong)",
pearsons_r > -0.6 & pearsons_r <= -0.2 ~ "Negative")) %>%
spread(-pearsons_r, -variable) %>%
mutate(Variable = "Experimentation Likelihood")) %>%
rbind(., d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(mean_creative_output, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-mean_creative_output, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Income",
variable == "rent_t2" ~ "Rent")) %>%
filter(is.na(mean_creative_output) == FALSE) %>%
group_by(variable) %>%
summarize(pearsons_r = cor(as.numeric(value), mean_creative_output, method = "pearson")) %>%
mutate(pearsons_r = case_when(pearsons_r >= 0.6 ~ "Positive (Strong)",
pearsons_r < 0.6 & pearsons_r >= 0.2 ~ "Positive",
pearsons_r < 0.2 & pearsons_r > -0.2 ~ "No Correlation",
pearsons_r <= -0.6 ~ "Negative (Strong)",
pearsons_r > -0.6 & pearsons_r <= -0.2 ~ "Negative")) %>%
spread(-pearsons_r, -variable) %>%
mutate(Variable = "Creative Output Likelihood")) %>%
rbind(., d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(mean_promotion, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-mean_promotion, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Income",
variable == "rent_t2" ~ "Rent")) %>%
filter(is.na(mean_promotion) == FALSE) %>%
group_by(variable) %>%
summarize(pearsons_r = cor(as.numeric(value), mean_promotion, method = "pearson")) %>%
mutate(pearsons_r = case_when(pearsons_r >= 0.6 ~ "Positive (Strong)",
pearsons_r < 0.6 & pearsons_r >= 0.2 ~ "Positive",
pearsons_r < 0.2 & pearsons_r > -0.2 ~ "No Correlation",
pearsons_r <= -0.6 ~ "Negative (Strong)",
pearsons_r > -0.6 & pearsons_r <= -0.2 ~ "Negative")) %>%
spread(-pearsons_r, -variable) %>%
mutate(Variable = "Artistic Promotion Likelihood")) %>%
rbind(., d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(mean_community_focus, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2, mean_ped_freq, income_t2, rent_t2) %>%
gather(-mean_community_focus, key = "variable", value = "value") %>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Stations / km2",
variable == "pop_per_km2_t2" ~ "Population / km2",
variable == "mean_ped_freq" ~ "Mean Pedestrian Frequency (Max - 3)",
variable == "pop_youth_pct_t2" ~ "Percentage of Young Adults",
variable == "income_t2" ~ "Income",
variable == "rent_t2" ~ "Rent")) %>%
filter(is.na(mean_community_focus) == FALSE) %>%
group_by(variable) %>%
summarize(pearsons_r = cor(as.numeric(value), mean_community_focus, method = "pearson")) %>%
mutate(pearsons_r = case_when(pearsons_r >= 0.6 ~ "Positive (Strong)",
pearsons_r < 0.6 & pearsons_r >= 0.2 ~ "Positive",
pearsons_r < 0.2 & pearsons_r > -0.2 ~ "No Correlation",
pearsons_r <= -0.6 ~ "Negative (Strong)",
pearsons_r > -0.6 & pearsons_r <= -0.2 ~ "Negative")) %>%
spread(-pearsons_r, -variable) %>%
mutate(Variable = "Community Focus Likelihood")) %>%
kable() %>%
kable_styling()| Income | Mean Pedestrian Frequency (Max - 3) | Percentage of Young Adults | Population / km2 | Rent | Transit Stations / km2 | Variable |
|---|---|---|---|---|---|---|
| Positive | Positive | Positive | Positive (Strong) | Positive (Strong) | Positive | Venue Density |
| Negative | Positive | Negative | Positive | Negative | No Correlation | Experimentation Likelihood |
| Negative | Positive | Negative | No Correlation | Negative | Negative (Strong) | Creative Output Likelihood |
| Negative | Positive | Negative | No Correlation | Negative | Negative (Strong) | Artistic Promotion Likelihood |
| Negative | Positive | No Correlation | Positive | Negative | No Correlation | Community Focus Likelihood |
6.5. Multi-city Density Comparison
Presently (8/26/2024) the Copenhagen analysis does not include Frederiksberg, so total population and venue numbers reflect only those in the relevant districts themselves.
Copenhagen has a relatively high per-capita and per area density of venues among CFP cities. These densities are higher than any city except Sydney (where most of the city was excluded from the study, so densities are artificially high). Those venues are relatively widely distributed - no single district ranks among the top 25 districts in the 8-city CFP sample.
d1_aggregates %>%
as.data.frame() %>%
filter(city != "Sydney") %>%
group_by(city) %>%
summarize(total_pop = sum(pop_t2),
total_venues = sum(venue_count, na.rm = TRUE),
area_sqkm = sum(area_km2)) %>%
rbind(.,
d2_aggregates_all %>%
as.data.frame() %>%
filter(city %in% c("Sydney", "Rotterdam")) %>%
group_by(city) %>%
summarize(total_pop = sum(pop_t2),
total_venues = sum(venue_count, na.rm = TRUE),
area_sqkm = sum(area_km2))) %>%
rbind(.,
d2_aggregates %>%
as.data.frame() %>%
mutate(city = "Copenhagen") %>%
group_by(city) %>%
summarize(total_pop = sum(as.numeric(pop_t2)),
total_venues = sum(venue_count, na.rm = TRUE),
area_sqkm = sum(area_km2))) %>%
mutate(venues_per_10k = total_venues / (total_pop/10000),
venue_sqkm = total_venues / area_sqkm) %>% arrange(desc(total_pop)) %>%
kable(
col.names = c("City","Population","Number of Venues","Area (sq km)","Number of Venues per 10k People","Venues Density (/sq km)")
) %>%
kable_styling()| City | Population | Number of Venues | Area (sq km) | Number of Venues per 10k People | Venues Density (/sq km) |
|---|---|---|---|---|---|
| 東京都 | 9272740 | 581 | 656.71911 | 0.6265678 | 0.8847009 |
| New York | 8426743 | 493 | 1211.89980 | 0.5850422 | 0.4067993 |
| Berlin | 3517424 | 495 | 887.57482 | 1.4072799 | 0.5576995 |
| Montreal | 2004265 | 265 | 616.72228 | 1.3221805 | 0.4296910 |
| Stockholm | 975551 | 96 | 214.58924 | 0.9840593 | 0.4473663 |
| Rotterdam | 650505 | 67 | 290.13636 | 1.0299690 | 0.2309259 |
| Copenhagen | 648036 | 108 | 92.35782 | 1.6665741 | 1.1693650 |
| Sydney | 394178 | 240 | 70.44623 | 6.0886199 | 3.4068536 |
d1_aggregates %>%
as.data.frame() %>%
filter(city != "Sydney" &
city != "New York") %>%
select(city, d1_name, venue_count, area_km2) %>%
rbind(.,
d2_aggregates_all %>%
as.data.frame() %>%
filter(city %in% c("Sydney", "New York", "Rotterdam")) %>%
select(city, venue_count, d2_name, area_km2) %>%
rename(d1_name = d2_name)) %>%
rbind(., d2_aggregates %>%
as.data.frame() %>%
select(venue_count, d2_name, area_km2) %>%
rename(d1_name = d2_name) %>%
mutate(city = "Copenhagen")) %>%
mutate(venue_density = venue_count / area_km2) %>%
arrange(-venue_density) %>%
mutate(rank = row_number()) %>%
kable(
col.names = c("City","District Name","Venue Count", "Area (km2)", "Venues/km2", "Density Rank")
) %>%
kable_styling() %>%
scroll_box(height = "200px")| City | District Name | Venue Count | Area (km2) | Venues/km2 | Density Rank |
|---|---|---|---|---|---|
| Sydney | Darlinghurst | 27 | 0.8573069 | 31.4939735 | 1 |
| New York | Midtown-Midtown South | 54 | 2.8002533 | 19.2839694 | 2 |
| New York | Chinatown | 31 | 1.6297224 | 19.0216447 | 3 |
| New York | East Village | 18 | 1.0105534 | 17.8120223 | 4 |
| Sydney | Sydney - Haymarket - The Rocks | 68 | 4.2948737 | 15.8328289 | 5 |
| Sydney | Potts Point - Woolloomooloo | 18 | 1.4600147 | 12.3286433 | 6 |
| New York | North Side-South Side | 32 | 2.6573944 | 12.0418707 | 7 |
| Sydney | Surry Hills | 15 | 1.3166746 | 11.3923361 | 8 |
| 東京都 | 渋谷区 | 167 | 15.1194158 | 11.0454003 | 9 |
| New York | West Village | 35 | 3.3916615 | 10.3194260 | 10 |
| Montreal | Le Plateau-Mont-Royal | 78 | 8.1341650 | 9.5891834 | 11 |
| Rotterdam | Rotterdam Centrum | 44 | 4.9064069 | 8.9678660 | 12 |
| Berlin | Friedrichshain-Kreuzberg | 172 | 20.2479799 | 8.4946746 | 13 |
| New York | Clinton | 20 | 2.5256447 | 7.9187702 | 14 |
| New York | Bushwick North | 18 | 2.3070437 | 7.8021931 | 15 |
| Sydney | Newtown - Camperdown - Darlington | 25 | 3.2819773 | 7.6173593 | 16 |
| New York | Central Harlem South | 10 | 1.3394146 | 7.4659479 | 17 |
| New York | Gramercy | 5 | 0.6940387 | 7.2042088 | 18 |
| New York | Bushwick South | 26 | 3.7333007 | 6.9643467 | 19 |
| Sydney | Redfern - Chippendale | 15 | 2.1633914 | 6.9335582 | 20 |
| New York | Hudson Yards-Chelsea-Flat Iron-Union Square | 27 | 4.7366269 | 5.7002589 | 21 |
| Montreal | Ville-Marie | 122 | 21.4545676 | 5.6864348 | 22 |
| 東京都 | 新宿区 | 87 | 18.2705806 | 4.7617534 | 23 |
| Sydney | Pyrmont - Ultimo | 7 | 1.4915006 | 4.6932598 | 24 |
| Stockholm | Norrmalm | 26 | 5.5474592 | 4.6868304 | 25 |
| New York | Brooklyn Heights-Cobble Hill | 4 | 0.9533316 | 4.1958117 | 26 |
| Copenhagen | Nørrebro | 17 | 4.0863623 | 4.1601794 | 27 |
| Copenhagen | Indre By | 42 | 10.4183129 | 4.0313629 | 28 |
| New York | SoHo-TriBeCa-Civic Center-Little Italy | 12 | 3.0928997 | 3.8798542 | 29 |
| New York | Upper East Side-Carnegie Hill | 7 | 1.8619321 | 3.7595357 | 30 |
| New York | East Williamsburg | 14 | 3.8341362 | 3.6514092 | 31 |
| 東京都 | 港区 | 79 | 24.4581428 | 3.2300081 | 32 |
| Stockholm | Södermalm | 33 | 10.3189928 | 3.1979865 | 33 |
| New York | Greenpoint | 11 | 3.4764871 | 3.1641136 | 34 |
| New York | Prospect Heights | 3 | 0.9591657 | 3.1277182 | 35 |
| Berlin | Mitte | 120 | 39.3129068 | 3.0524326 | 36 |
| New York | Park Slope-Gowanus | 12 | 3.9437843 | 3.0427628 | 37 |
| New York | Lincoln Square | 6 | 2.1100959 | 2.8434726 | 38 |
| Sydney | Glebe - Forest Lodge | 6 | 2.3023174 | 2.6060699 | 39 |
| Sydney | Paddington - Moore Park | 9 | 3.7256775 | 2.4156680 | 40 |
| New York | Stuyvesant Heights | 7 | 2.9141911 | 2.4020387 | 41 |
| New York | Parkchester | 2 | 0.8518291 | 2.3478888 | 42 |
| Sydney | Petersham - Stanmore | 7 | 3.0036443 | 2.3305023 | 43 |
| New York | Erasmus | 3 | 1.3482579 | 2.2250936 | 44 |
| New York | Manhattanville | 3 | 1.4154807 | 2.1194214 | 45 |
| 東京都 | 豊島区 | 27 | 12.9723488 | 2.0813501 | 46 |
| New York | Marble Hill-Inwood | 4 | 1.9680156 | 2.0325042 | 47 |
| New York | Lower East Side | 8 | 4.0192714 | 1.9904105 | 48 |
| New York | Bedford | 6 | 3.0276690 | 1.9817226 | 49 |
| New York | Fort Greene | 3 | 1.5308090 | 1.9597481 | 50 |
| Copenhagen | Vesterbro/Kongens Enghave | 16 | 8.2202277 | 1.9464181 | 51 |
| New York | Ridgewood | 9 | 4.6736880 | 1.9256741 | 52 |
| Sydney | Erskineville - Alexandria | 8 | 4.3214310 | 1.8512386 | 53 |
| New York | Turtle Bay-East Midtown | 5 | 2.7382373 | 1.8259922 | 54 |
| Sydney | Marrickville | 10 | 5.8143386 | 1.7198861 | 55 |
| Sydney | Lilyfield - Rozelle | 6 | 3.6052202 | 1.6642534 | 56 |
| New York | Clinton Hill | 3 | 1.9022405 | 1.5770877 | 57 |
| 東京都 | 中央区 | 17 | 10.9365767 | 1.5544169 | 58 |
| Sydney | Balmain | 4 | 2.5818140 | 1.5492983 | 59 |
| New York | Central Harlem North-Polo Grounds | 4 | 2.6257977 | 1.5233466 | 60 |
| 東京都 | 杉並区 | 46 | 33.9314315 | 1.3556752 | 61 |
| Sydney | Leichhardt - Annandale | 6 | 4.4737785 | 1.3411482 | 62 |
| 東京都 | 千代田区 | 15 | 11.3650272 | 1.3198385 | 63 |
| Berlin | Neukölln | 59 | 44.7557902 | 1.3182652 | 64 |
| 東京都 | 台東区 | 13 | 10.0868731 | 1.2888038 | 65 |
| Montreal | Le Sud-Ouest | 23 | 18.1055520 | 1.2703286 | 66 |
| New York | Mott Haven-Port Morris | 6 | 4.9820337 | 1.2043275 | 67 |
| Copenhagen | Bispebjerg | 8 | 6.7957940 | 1.1771987 | 68 |
| New York | DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill | 3 | 2.6205028 | 1.1448185 | 69 |
| 東京都 | 世田谷区 | 64 | 58.1264075 | 1.1010486 | 70 |
| New York | Crown Heights North | 5 | 4.7919635 | 1.0434136 | 71 |
| New York | Spuyten Duyvil-Kingsbridge | 3 | 2.9183674 | 1.0279720 | 72 |
| New York | Morningside Heights | 3 | 2.9194324 | 1.0275970 | 73 |
| Copenhagen | Østerbro | 9 | 9.7229774 | 0.9256424 | 74 |
| New York | Murray Hill-Kips Bay | 2 | 2.1786399 | 0.9180039 | 75 |
| Stockholm | Östermalm | 21 | 23.9306360 | 0.8775362 | 76 |
| Rotterdam | Delfshaven | 5 | 5.9304907 | 0.8431006 | 77 |
| New York | Upper West Side | 4 | 4.8152846 | 0.8306882 | 78 |
| Montreal | Rosemont-La Petite-Patrie | 13 | 15.8523232 | 0.8200691 | 79 |
| Sydney | Ashfield | 3 | 3.6704585 | 0.8173366 | 80 |
| New York | Co-op City | 3 | 3.7074211 | 0.8091878 | 81 |
| Montreal | Outremont | 3 | 3.8051755 | 0.7883999 | 82 |
| New York | Stuyvesant Town-Cooper Village | 1 | 1.3061074 | 0.7656338 | 83 |
| Sydney | Dulwich Hill - Lewisham | 2 | 2.7024868 | 0.7400591 | 84 |
| 東京都 | 中野区 | 11 | 15.5821578 | 0.7059356 | 85 |
| Rotterdam | Feijenoord | 6 | 8.5230683 | 0.7039718 | 86 |
| New York | Prospect Lefferts Gardens-Wingate | 2 | 2.9357728 | 0.6812516 | 87 |
| New York | Crown Heights South | 1 | 1.4831464 | 0.6742423 | 88 |
| New York | Highbridge | 1 | 1.5081489 | 0.6630645 | 89 |
| New York | Crotona Park East | 1 | 1.5298447 | 0.6536611 | 90 |
| New York | East Flatbush-Farragut | 2 | 3.1870228 | 0.6275449 | 91 |
| 東京都 | 文京区 | 7 | 11.3704312 | 0.6156319 | 92 |
| 東京都 | 目黒区 | 9 | 14.8401749 | 0.6064619 | 93 |
| New York | Hunts Point | 4 | 6.5990226 | 0.6061504 | 94 |
| New York | East Concourse-Concourse Village | 1 | 1.6918630 | 0.5910644 | 95 |
| New York | West Concourse | 1 | 1.7683954 | 0.5654844 | 96 |
| Stockholm | Enskede-Årsta-Vantör | 12 | 21.4558373 | 0.5592884 | 97 |
| Rotterdam | Noord | 3 | 5.3672085 | 0.5589498 | 98 |
| Sydney | Waterloo - Beaconsfield | 2 | 3.5822053 | 0.5583153 | 99 |
| Copenhagen | Amager Øst | 5 | 9.2973689 | 0.5377866 | 100 |
| New York | Sunset Park West | 5 | 9.3969302 | 0.5320887 | 101 |
| Berlin | Pankow | 54 | 102.6967098 | 0.5258202 | 102 |
| Sydney | Sydenham - Tempe - St Peters | 2 | 3.8551239 | 0.5187901 | 103 |
| Berlin | Tempelhof-Schöneberg | 27 | 52.9136174 | 0.5102656 | 104 |
| New York | Washington Heights North | 2 | 3.9690593 | 0.5038977 | 105 |
| New York | Elmhurst-Maspeth | 1 | 2.0280964 | 0.4930732 | 106 |
| Montreal | Villeray-Saint-Michel-Parc-Extension | 8 | 16.4418483 | 0.4865633 | 107 |
| Copenhagen | Vanløse | 3 | 6.6652325 | 0.4500968 | 108 |
| New York | Queensbridge-Ravenswood-Long Island City | 1 | 2.2230057 | 0.4498414 | 109 |
| New York | Westchester-Unionport | 1 | 2.2507352 | 0.4442993 | 110 |
| Rotterdam | Charlois | 5 | 11.8990066 | 0.4202031 | 111 |
| New York | East Harlem South | 1 | 2.3844742 | 0.4193797 | 112 |
| New York | Van Cortlandt Village | 1 | 2.3941893 | 0.4176779 | 113 |
| New York | Carroll Gardens-Columbia Street-Red Hook | 4 | 9.7929812 | 0.4084558 | 114 |
| New York | Woodside | 1 | 2.6240659 | 0.3810880 | 115 |
| 東京都 | 墨田区 | 5 | 13.7417454 | 0.3638548 | 116 |
| New York | Allerton-Pelham Gardens | 1 | 2.9382851 | 0.3403346 | 117 |
| New York | Brownsville | 1 | 3.0332438 | 0.3296801 | 118 |
| Montreal | Mercier-Hochelaga-Maisonneuve | 9 | 27.3492733 | 0.3290764 | 119 |
| Copenhagen | Valby | 3 | 9.1885094 | 0.3264947 | 120 |
| Berlin | Charlottenburg-Wilmersdorf | 21 | 64.4375733 | 0.3258968 | 121 |
| New York | Seagate-Coney Island | 2 | 6.2456601 | 0.3202224 | 122 |
| New York | Maspeth | 1 | 3.3213158 | 0.3010855 | 123 |
| New York | Washington Heights South | 1 | 3.3495690 | 0.2985459 | 124 |
| 東京都 | 品川区 | 8 | 27.0119411 | 0.2961653 | 125 |
| New York | Kew Gardens Hills | 1 | 3.5287973 | 0.2833827 | 126 |
| Montreal | Côte-des-Neiges-Notre-Dame-de-Grâce | 6 | 21.4378047 | 0.2798794 | 127 |
| New York | Woodlawn-Wakefield | 1 | 3.6446790 | 0.2743726 | 128 |
| New York | Astoria | 1 | 3.6495687 | 0.2740050 | 129 |
| New York | Bensonhurst East | 1 | 3.6718804 | 0.2723400 | 130 |
| New York | South Jamaica | 1 | 3.7140168 | 0.2692503 | 131 |
| New York | Battery Park City-Lower Manhattan | 1 | 3.7381125 | 0.2675147 | 132 |
| New York | Flatbush | 1 | 4.1978295 | 0.2382183 | 133 |
| New York | New Brighton-Silver Lake | 1 | 4.3793368 | 0.2283451 | 134 |
| New York | Jamaica | 1 | 4.3848573 | 0.2280576 | 135 |
| New York | West New Brighton-New Brighton-St. George | 2 | 8.8469541 | 0.2260665 | 136 |
| New York | Schuylerville-Throgs Neck-Edgewater Park | 4 | 18.8174972 | 0.2125681 | 137 |
| New York | Richmond Hill | 1 | 4.7353359 | 0.2111783 | 138 |
| Copenhagen | Amager Vest | 4 | 19.2663407 | 0.2076160 | 139 |
| New York | Hunters Point-Sunnyside-West Maspeth | 2 | 9.9182923 | 0.2016476 | 140 |
| Berlin | Lichtenberg | 9 | 51.9383369 | 0.1732824 | 141 |
| Rotterdam | IJsselmonde | 2 | 13.1009552 | 0.1526606 | 142 |
| New York | Steinway | 1 | 6.8263689 | 0.1464908 | 143 |
| 東京都 | 北区 | 3 | 20.4957190 | 0.1463720 | 144 |
| New York | North Riverdale-Fieldston-Riverdale | 1 | 6.9567470 | 0.1437453 | 145 |
| New York | Sheepshead Bay-Gerritsen Beach-Manhattan Beach | 1 | 7.3791060 | 0.1355178 | 146 |
| 東京都 | 江東区 | 7 | 55.2347111 | 0.1267319 | 147 |
| 東京都 | 練馬区 | 6 | 48.1419087 | 0.1246315 | 148 |
| Stockholm | Hägersten-Älvsjö | 3 | 24.4447288 | 0.1227258 | 149 |
| Copenhagen | Brønshøj-Husum | 1 | 8.6966948 | 0.1149862 | 150 |
| New York | College Point | 1 | 8.7223805 | 0.1146476 | 151 |
| Berlin | Treptow-Köpenick | 18 | 167.0701970 | 0.1077391 | 152 |
| Stockholm | Skärholmen | 1 | 10.1425427 | 0.0985946 | 153 |
| Berlin | Marzahn-Hellersdorf | 6 | 61.5770031 | 0.0974390 | 154 |
| 東京都 | 江戸川区 | 4 | 49.3394958 | 0.0810710 | 155 |
| Rotterdam | Kralingen-Crooswijk | 1 | 12.7215597 | 0.0786067 | 156 |
| Montreal | Saint-Léonard | 1 | 13.5213926 | 0.0739569 | 157 |
| 東京都 | 大田区 | 5 | 75.1911276 | 0.0664972 | 158 |
| Berlin | Spandau | 6 | 91.5120393 | 0.0655651 | 159 |
| New York | New Springville-Bloomfield-Travis | 2 | 31.9838943 | 0.0625315 | 160 |
| Rotterdam | Prins Alexander | 1 | 18.6627216 | 0.0535828 | 161 |
| Montreal | Lachine | 1 | 23.0784749 | 0.0433304 | 162 |
| Montreal | Ahuntsic-Cartierville | 1 | 25.5160844 | 0.0391910 | 163 |
| Berlin | Reinickendorf | 3 | 88.9636853 | 0.0337216 | 164 |
| New York | park-cemetery-etc-Bronx | 1 | 29.9930264 | 0.0333411 | 165 |
| 東京都 | 足立区 | 1 | 53.2578235 | 0.0187766 | 166 |
| 東京都 | 荒川区 | NA | 10.2287383 | NA | 167 |
| 東京都 | 板橋区 | NA | 32.1918125 | NA | 168 |
| 東京都 | 葛飾区 | NA | 34.8245166 | NA | 169 |
| Berlin | Steglitz-Zehlendorf | NA | 102.1489826 | NA | 170 |
| Stockholm | Kungsholmen | NA | 6.9429741 | NA | 171 |
| Stockholm | Farsta | NA | 17.1135265 | NA | 172 |
| Stockholm | Bromma | NA | 27.6206531 | NA | 173 |
| Stockholm | Hässelby-Vällingby | NA | 24.8591388 | NA | 174 |
| Stockholm | Skarpnäck | NA | 17.4620933 | NA | 175 |
| Stockholm | Spånga-Tensta | NA | 12.8603071 | NA | 176 |
| Stockholm | Rinkeby-Kista | NA | 11.8903550 | NA | 177 |
| Montreal | LaSalle | NA | 25.1437195 | NA | 178 |
| Montreal | Mont-Royal | NA | 7.4295857 | NA | 179 |
| Montreal | Hampstead | NA | 1.7642744 | NA | 180 |
| Montreal | Rivière-des-Prairies-Pointe-aux-Trembles | NA | 49.9380360 | NA | 181 |
| Montreal | Dorval | NA | 28.0959506 | NA | 182 |
| Montreal | Montréal-Nord | NA | 12.4032778 | NA | 183 |
| Montreal | L’Île-Bizard-Sainte-Geneviève | NA | 36.4534924 | NA | 184 |
| Montreal | Kirkland | NA | 9.6667690 | NA | 185 |
| Montreal | Dollard-des-Ormeaux | NA | 15.0327412 | NA | 186 |
| Montreal | Senneville | NA | 18.5697951 | NA | 187 |
| Montreal | Côte-Saint-Luc | NA | 6.7956587 | NA | 188 |
| Montreal | Montréal-Ouest | NA | 1.4164230 | NA | 189 |
| Montreal | Pointe-Claire | NA | 34.3728005 | NA | 190 |
| Montreal | L’Île-Dorval | NA | 0.1801239 | NA | 191 |
| Montreal | Saint-Laurent | NA | 42.9853936 | NA | 192 |
| Montreal | Beaconsfield | NA | 24.8691706 | NA | 193 |
| Montreal | Westmount | NA | 4.0077149 | NA | 194 |
| Montreal | Montréal-Est | NA | 13.9436851 | NA | 195 |
| Montreal | Anjou | NA | 13.8481238 | NA | 196 |
| Montreal | Pierrefonds-Roxboro | NA | 33.6924740 | NA | 197 |
| Montreal | Sainte-Anne-de-Bellevue | NA | 11.1265670 | NA | 198 |
| Montreal | Verdun | NA | 22.2811389 | NA | 199 |
| Montreal | Baie-d’Urfé | NA | 8.0087000 | NA | 200 |
| New York | Brighton Beach | NA | 1.8937054 | NA | 201 |
| New York | West Brighton | NA | 0.8134721 | NA | 202 |
| New York | Homecrest | NA | 2.7826312 | NA | 203 |
| New York | Gravesend | NA | 3.3490590 | NA | 204 |
| New York | Bath Beach | NA | 2.4803128 | NA | 205 |
| New York | Bensonhurst West | NA | 4.3962015 | NA | 206 |
| New York | Dyker Heights | NA | 2.7740491 | NA | 207 |
| New York | Bay Ridge | NA | 12.9486553 | NA | 208 |
| New York | Sunset Park East | NA | 2.5135895 | NA | 209 |
| New York | Windsor Terrace | NA | 1.3030339 | NA | 210 |
| New York | Kensington-Ocean Parkway | NA | 1.4746683 | NA | 211 |
| New York | Midwood | NA | 3.3226713 | NA | 212 |
| New York | Madison | NA | 2.5403645 | NA | 213 |
| New York | Georgetown-Marine Park-Bergen Beach-Mill Basin | NA | 8.1416548 | NA | 214 |
| New York | Ocean Parkway South | NA | 1.6540101 | NA | 215 |
| New York | Canarsie | NA | 8.4307076 | NA | 216 |
| New York | Flatlands | NA | 5.0497382 | NA | 217 |
| New York | Williamsburg | NA | 1.0754648 | NA | 218 |
| New York | Ocean Hill | NA | 1.8627183 | NA | 219 |
| New York | East New York | NA | 11.7778366 | NA | 220 |
| New York | Cypress Hills-City Line | NA | 2.5532849 | NA | 221 |
| New York | East New York (Pennsylvania Ave) | NA | 1.8029009 | NA | 222 |
| New York | Borough Park | NA | 5.0074899 | NA | 223 |
| New York | Starrett City | NA | 1.2066294 | NA | 224 |
| New York | Rugby-Remsen Village | NA | 3.0292675 | NA | 225 |
| New York | park-cemetery-etc-Brooklyn | NA | 65.2042848 | NA | 226 |
| New York | Claremont-Bathgate | NA | 1.5248039 | NA | 227 |
| New York | Eastchester-Edenwald-Baychester | NA | 3.7251272 | NA | 228 |
| New York | Bedford Park-Fordham North | NA | 1.3895957 | NA | 229 |
| New York | Belmont | NA | 1.2664631 | NA | 230 |
| New York | Bronxdale | NA | 1.4107283 | NA | 231 |
| New York | West Farms-Bronx River | NA | 1.3932731 | NA | 232 |
| New York | Soundview-Castle Hill-Clason Point-Harding Park | NA | 7.5660253 | NA | 233 |
| New York | Pelham Bay-Country Club-City Island | NA | 12.1768064 | NA | 234 |
| New York | East Tremont | NA | 1.7998605 | NA | 235 |
| New York | Kingsbridge Heights | NA | 1.2430887 | NA | 236 |
| New York | Longwood | NA | 0.9965086 | NA | 237 |
| New York | Melrose South-Mott Haven North | NA | 1.6038093 | NA | 238 |
| New York | Morrisania-Melrose | NA | 1.5660179 | NA | 239 |
| New York | University Heights-Morris Heights | NA | 1.9881846 | NA | 240 |
| New York | Van Nest-Morris Park-Westchester Square | NA | 3.3531194 | NA | 241 |
| New York | Fordham South | NA | 0.5845523 | NA | 242 |
| New York | Mount Hope | NA | 1.3641714 | NA | 243 |
| New York | Norwood | NA | 1.4587939 | NA | 244 |
| New York | Williamsbridge-Olinville | NA | 3.3677750 | NA | 245 |
| New York | Pelham Parkway | NA | 2.1465052 | NA | 246 |
| New York | Soundview-Bruckner | NA | 1.5012932 | NA | 247 |
| New York | Rikers Island | NA | 2.7017658 | NA | 248 |
| New York | Hamilton Heights | NA | 2.3087123 | NA | 249 |
| New York | Lenox Hill-Roosevelt Island | NA | 3.1099915 | NA | 250 |
| New York | Yorkville | NA | 1.8890459 | NA | 251 |
| New York | East Harlem North | NA | 2.9629487 | NA | 252 |
| New York | park-cemetery-etc-Manhattan | NA | 14.0007113 | NA | 253 |
| New York | Springfield Gardens North | NA | 2.6384820 | NA | 254 |
| New York | Springfield Gardens South-Brookville | NA | 4.0421927 | NA | 255 |
| New York | Rosedale | NA | 5.7313477 | NA | 256 |
| New York | Jamaica Estates-Holliswood | NA | 3.9788122 | NA | 257 |
| New York | Hollis | NA | 2.1224191 | NA | 258 |
| New York | St. Albans | NA | 7.1893115 | NA | 259 |
| New York | Breezy Point-Belle Harbor-Rockaway Park-Broad Channel | NA | 17.4701853 | NA | 260 |
| New York | Hammels-Arverne-Edgemere | NA | 10.8549352 | NA | 261 |
| New York | Far Rockaway-Bayswater | NA | 6.5394274 | NA | 262 |
| New York | Forest Hills | NA | 5.3685110 | NA | 263 |
| New York | Rego Park | NA | 1.8420460 | NA | 264 |
| New York | Glendale | NA | 2.7762328 | NA | 265 |
| New York | Middle Village | NA | 5.3728392 | NA | 266 |
| New York | Flushing | NA | 3.5178239 | NA | 267 |
| New York | Corona | NA | 1.8703544 | NA | 268 |
| New York | North Corona | NA | 1.6702686 | NA | 269 |
| New York | East Elmhurst | NA | 1.7926989 | NA | 270 |
| New York | Jackson Heights | NA | 4.4515635 | NA | 271 |
| New York | Elmhurst | NA | 3.0325558 | NA | 272 |
| New York | Cambria Heights | NA | 3.1203988 | NA | 273 |
| New York | Queens Village | NA | 6.5122115 | NA | 274 |
| New York | Briarwood-Jamaica Hills | NA | 2.7220111 | NA | 275 |
| New York | Pomonok-Flushing Heights-Hillcrest | NA | 3.6077920 | NA | 276 |
| New York | Fresh Meadows-Utopia | NA | 2.5721683 | NA | 277 |
| New York | Oakland Gardens | NA | 4.7162745 | NA | 278 |
| New York | Bellerose | NA | 5.0881230 | NA | 279 |
| New York | Glen Oaks-Floral Park-New Hyde Park | NA | 4.2337202 | NA | 280 |
| New York | Douglas Manor-Douglaston-Little Neck | NA | 7.3529631 | NA | 281 |
| New York | Bayside-Bayside Hills | NA | 8.1381071 | NA | 282 |
| New York | Ft. Totten-Bay Terrace-Clearview | NA | 8.8947762 | NA | 283 |
| New York | Auburndale | NA | 3.1742823 | NA | 284 |
| New York | Whitestone | NA | 9.4011905 | NA | 285 |
| New York | Murray Hill | NA | 4.8704594 | NA | 286 |
| New York | East Flushing | NA | 2.7341558 | NA | 287 |
| New York | Woodhaven | NA | 3.4480840 | NA | 288 |
| New York | South Ozone Park | NA | 7.5912561 | NA | 289 |
| New York | Ozone Park | NA | 2.3294517 | NA | 290 |
| New York | Lindenwood-Howard Beach | NA | 7.1703050 | NA | 291 |
| New York | Kew Gardens | NA | 1.8986363 | NA | 292 |
| New York | Queensboro Hill | NA | 2.4499584 | NA | 293 |
| New York | Laurelton | NA | 3.6747951 | NA | 294 |
| New York | Old Astoria | NA | 1.4729512 | NA | 295 |
| New York | Baisley Park | NA | 4.0884902 | NA | 296 |
| New York | Airport | NA | 24.3885253 | NA | 297 |
| New York | park-cemetery-etc-Queens | NA | 172.7338831 | NA | 298 |
| New York | Annadale-Huguenot-Prince’s Bay-Eltingville | NA | 19.6291150 | NA | 299 |
| New York | Westerleigh | NA | 5.8636530 | NA | 300 |
| New York | Grymes Hill-Clifton-Fox Hills | NA | 3.5181857 | NA | 301 |
| New York | Charleston-Richmond Valley-Tottenville | NA | 18.6336298 | NA | 302 |
| New York | Mariner’s Harbor-Arlington-Port Ivory-Graniteville | NA | 9.8380216 | NA | 303 |
| New York | Grasmere-Arrochar-Ft. Wadsworth | NA | 5.6777242 | NA | 304 |
| New York | Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill | NA | 17.1375623 | NA | 305 |
| New York | Oakwood-Oakwood Beach | NA | 6.1663325 | NA | 306 |
| New York | Port Richmond | NA | 3.9243841 | NA | 307 |
| New York | Rossville-Woodrow | NA | 6.0258564 | NA | 308 |
| New York | Old Town-Dongan Hills-South Beach | NA | 7.6134784 | NA | 309 |
| New York | Stapleton-Rosebank | NA | 8.3703705 | NA | 310 |
| New York | New Dorp-Midland Beach | NA | 6.2090210 | NA | 311 |
| New York | Arden Heights | NA | 4.6787025 | NA | 312 |
| New York | Great Kills | NA | 11.0024987 | NA | 313 |
| New York | park-cemetery-etc-Staten Island | NA | 85.1199744 | NA | 314 |
| Sydney | Burwood - Croydon | NA | 4.4941239 | NA | 315 |
| Sydney | Croydon Park - Enfield | NA | 3.9723088 | NA | 316 |
| Sydney | Haberfield - Summer Hill | NA | 3.4755633 | NA | 317 |
| Rotterdam | Overschie | NA | 17.3443818 | NA | 318 |
| Rotterdam | Hillegersberg-Schiebroek | NA | 13.2748738 | NA | 319 |
| Rotterdam | Pernis | NA | 1.5937589 | NA | 320 |
| Rotterdam | Hoogvliet | NA | 10.3442731 | NA | 321 |
| Rotterdam | Hoek van Holland | NA | 18.5904905 | NA | 322 |
| Rotterdam | Waalhaven-Eemhaven | NA | 15.0211502 | NA | 323 |
| Rotterdam | Vondelingenplaat | NA | 9.4912269 | NA | 324 |
| Rotterdam | Botlek-Europoort-Maasvlakte | NA | 115.5976172 | NA | 325 |
| Rotterdam | Rotterdam-Noord-West | NA | 1.1716582 | NA | 326 |
| Rotterdam | Rivium | NA | 0.1146757 | NA | 327 |
| Rotterdam | Rozenburg | NA | 6.4808323 | NA | 328 |
6.5.1 - Point Pattern Analysis of clustering and centrality
Source: CFP
Copenhagen’s venues are more tightly clustered with one another than any other CFP city - this is very notable, and perhaps indicative of the human-scale transit accessibility the city is known for.,
Average nn distance between a venue and its two nearest neighbors (in meters).
venues_pattern <- main_venue_data %>%
filter(city == "Rotterdam", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_rotterdam) %>%
mutate(venue_nn2 = nn_function(st_coordinates(.),
st_coordinates(.), k = 3)) %>%
st_transform(4326) %>%
rbind(., main_venue_data %>%
filter(city == "Sydney", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_sydney) %>%
mutate(venue_nn2 = nn_function(st_coordinates(.),
st_coordinates(.), k = 3)) %>%
st_transform(4326)) %>%
rbind(., main_venue_data %>%
filter(city == "Stockholm", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_stockholm) %>%
mutate(venue_nn2 = nn_function(st_coordinates(.),
st_coordinates(.), k = 3)) %>%
st_transform(4326)) %>%
rbind(., main_venue_data %>%
filter(city == "Tokyo", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_tokyo) %>%
mutate(venue_nn2 = nn_function(st_coordinates(.),
st_coordinates(.), k = 3)) %>%
st_transform(4326)) %>%
rbind(., main_venue_data %>%
filter(city == "Berlin", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_berlin) %>%
mutate(venue_nn2 = nn_function(st_coordinates(.),
st_coordinates(.), k = 3)) %>%
st_transform(4326)) %>%
rbind(., main_venue_data %>%
filter(city == "Montreal", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_montreal) %>%
mutate(venue_nn2 = nn_function(st_coordinates(.),
st_coordinates(.), k = 3)) %>%
st_transform(4326)) %>%
rbind(., main_venue_data %>%
filter(city == "New York", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_new_york) %>%
mutate(venue_nn2 = nn_function(st_coordinates(.),
st_coordinates(.), k = 3) / 3.28) %>%
st_transform(4326)) %>%
rbind(., main_venue_data %>%
filter(city == "Copenhagen", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_copenhagen) %>%
mutate(venue_nn2 = nn_function(st_coordinates(.),
st_coordinates(.), k = 3) / 3.28) %>%
st_transform(4326))
venues_pattern %>%
as.data.frame() %>%
group_by(city) %>%
summarize(mean_knn2_m = mean(venue_nn2),
median_knn2_m = median(venue_nn2)) %>%
kable() %>%
kable_styling()| city | mean_knn2_m | median_knn2_m |
|---|---|---|
| Berlin | 234.21637 | 96.06403 |
| Copenhagen | 82.03627 | 47.32980 |
| Montreal | 179.53679 | 98.11968 |
| New York | 255.98960 | 119.67304 |
| Rotterdam | 263.13005 | 131.46448 |
| Stockholm | 279.17632 | 126.71238 |
| Sydney | 137.42448 | 92.92099 |
| Tokyo | 183.46044 | 70.81106 |
On average, venues in Copenhagen are relatively dispersed IN GENERAL, but they are locally very clustered. This presents excellent opportunities for managed distircts, neighborhood-centered scenes. Global clustering is measured here as average distance to the the centroid of the venue cluster - the average X,Y location point).
centers <- main_venue_data %>%
filter(is.na(x) == FALSE) %>%
group_by(city) %>%
summarize(mean_x = mean(x),
mean_y = mean(y))
venues_centroid_dist <- main_venue_data %>%
filter(city == "Rotterdam", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_rotterdam) %>%
mutate(centroid_dist = st_distance(., centers %>%
filter(city == "Rotterdam") %>%
st_as_sf(coords = c("mean_x", "mean_y"), crs = 4326) %>%
st_transform(epsg_rotterdam))) %>%
st_transform(4326) %>%
rbind(., main_venue_data %>%
filter(city == "Sydney", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_sydney) %>%
mutate(centroid_dist = st_distance(., centers %>%
filter(city == "Sydney") %>%
st_as_sf(coords = c("mean_x", "mean_y"), crs = 4326) %>%
st_transform(epsg_sydney))) %>%
st_transform(4326)) %>%
rbind(., main_venue_data %>%
filter(city == "Stockholm", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_stockholm) %>%
mutate(centroid_dist = st_distance(., centers %>%
filter(city == "Stockholm") %>%
st_as_sf(coords = c("mean_x", "mean_y"), crs = 4326) %>%
st_transform(epsg_stockholm))) %>%
st_transform(4326)) %>%
rbind(., main_venue_data %>%
filter(city == "Tokyo", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_tokyo) %>%
mutate(centroid_dist = st_distance(., centers %>%
filter(city == "Tokyo") %>%
st_as_sf(coords = c("mean_x", "mean_y"), crs = 4326) %>%
st_transform(epsg_tokyo))) %>%
st_transform(4326)) %>%
rbind(., main_venue_data %>%
filter(city == "Berlin", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_berlin) %>%
mutate(centroid_dist = st_distance(., centers %>%
filter(city == "Berlin") %>%
st_as_sf(coords = c("mean_x", "mean_y"), crs = 4326) %>%
st_transform(epsg_berlin))) %>%
st_transform(4326)) %>%
rbind(., main_venue_data %>%
filter(city == "Montreal", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_montreal) %>%
mutate(centroid_dist = st_distance(., centers %>%
filter(city == "Montreal") %>%
st_as_sf(coords = c("mean_x", "mean_y"), crs = 4326) %>%
st_transform(epsg_montreal))) %>%
st_transform(4326)) %>%
rbind(., main_venue_data %>%
filter(city == "New York", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_new_york) %>%
mutate(centroid_dist = st_distance(., centers %>%
filter(city == "New York") %>%
st_as_sf(coords = c("mean_x", "mean_y"), crs = 4326) %>%
st_transform(epsg_new_york)) / 3.28) %>%
st_transform(4326)) %>%
rbind(., main_venue_data %>%
filter(city == "Copenhagen", is.na(x) == FALSE) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_transform(epsg_copenhagen) %>%
mutate(centroid_dist = st_distance(., centers %>%
filter(city == "Copenhagen") %>%
st_as_sf(coords = c("mean_x", "mean_y"), crs = 4326) %>%
st_transform(epsg_copenhagen))) %>%
st_transform(4326))
venues_centroid_dist %>%
as.data.frame() %>%
group_by(city) %>%
summarize(mean_centroid_dist = mean(centroid_dist),
median_centroid_dist = median(centroid_dist)) %>%
kable() %>%
kable_styling()| city | mean_centroid_dist | median_centroid_dist |
|---|---|---|
| Berlin | 3484.748 | 2801.521 |
| Copenhagen | 2332.798 | 2215.211 |
| Montreal | 2172.774 | 1700.682 |
| New York | 5299.167 | 3612.162 |
| Rotterdam | 1465.930 | 1145.001 |
| Stockholm | 2066.532 | 1827.351 |
| Sydney | 2157.818 | 1926.532 |
| Tokyo | 4072.481 | 3099.027 |
7 Borough Thematic Mapping
Sources: CFP, City of Copenhagen, Resk (2020), Finans Danmark, OpenData DK
7.1 Urban Characteristics
7.1.1 Urban Characteristics - Quantile Ranks
This “heatmap” chart shows the relatively high or low values of urban variables by district.
These charts are useful to reference in the context of the district profiles in Section 9.
Norrebro really sticks out here as being atypical - it has very high programming scores, high venue density, and good transit access - it also has relatively lower incomes and property values. Usually we don’t see this combination.
# Get quartile values for each of the d1 aggregates for various characteristics
d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
#mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(d2_name, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2,
#mean_ped_freq,
income_t2, rent_t2, venue_dens) %>%
gather(-d2_name, key = "variable", value = "value") %>%
mutate(value = as.numeric(value)) %>%
group_by(variable) %>%
summarize(quantile = scales::percent(c(0.25, 0.5, 0.75)),
quartile = quantile(value, c(0.25, 0.5, 0.75), na.rm = TRUE)) %>%
spread(quantile, quartile) %>% # join it back to the original data analysis
left_join(d2_aggregates %>%
mutate(venue_dens = venue_count / area_km2,
# mean_ped_freq = ((3*ped_freq_3) + (2* ped_freq_2) + (1* ped_freq_1))/venue_count,
pop_youth_pct_t2 = 100 * pop_youth_pct_t2) %>%
as.data.frame() %>%
select(d2_name, transit_stations_per_km2, pop_per_km2_t2, pop_youth_pct_t2,
income_t2, rent_t2, venue_dens) %>%
gather(-d2_name, key = "variable", value = "value"), .) %>%
mutate(value = as.numeric(value)) %>%
mutate(quantile = case_when(value >= `75%` ~ 4,
value <= `25%` ~ 1,
value > `25%` & value < `50%` ~ 2,
value >= `50%` & value < `75%` ~ 3))%>%
mutate(variable = case_when(variable == "transit_stations_per_km2" ~ "Transit Density",
variable == "pop_per_km2_t2" ~ "Pop Density",
variable == "pop_youth_pct_t2" ~ "Pct Young Adults",
variable == "income_t2" ~ "Income",
variable == "rent_t2" ~ "Rent",
variable == "venue_dens" ~ "Venue Density")) %>%
ggplot()+
geom_tile(aes(y = d2_name, x = variable, fill = quantile),
alpha = 0.6)+ #flip coord, order desc
scale_fill_gradient(low = "white", high = CityPalette[5],
limits=c(1,4), breaks=c(1,4),
labels=c("Minimum","Maximum"),
name = "")+
coord_flip()+
labs(
title = "District Planning & Population Characteristics",
subtitle = "Districts with no venues have their density represented as grey",
caption = "Data: CFP, City of Copenhagen, Resk (2020), Finans Danmark, OpenData DK")+
#ylab("Quartile")+
#xlab("")+
plotTheme
7.1.2 Program and Space Charcteristics - Quantile Ranks
# Get quartile values for each of the d1 aggregates for various characteristics
d2_aggregates %>%
filter(venue_count > 0) %>%
mutate(venue_dens = venue_count / area_km2,
med_size = ((4* size_4) + (3*size_3) + (2* size_2) + (1* size_1))/venue_count) %>%
as.data.frame() %>%
select(d2_name, mean_experimentation, mean_creative_output, mean_community_focus, venue_dens, med_size, venue_count, mean_promotion) %>%
gather(-d2_name, key = "variable", value = "value") %>%
mutate(value = as.numeric(value)) %>%
group_by(variable) %>%
summarize(quantile = scales::percent(c(0.25, 0.5, 0.75)),
quartile = quantile(value, c(0.25, 0.5, 0.75), na.rm = TRUE)) %>%
spread(quantile, quartile) %>% # join it back to the original data analysis
left_join(d2_aggregates %>%
filter(venue_count > 0) %>%
mutate(venue_dens = venue_count / area_km2,
med_size = ((4* size_4) + (3*size_3) + (2* size_2) + (1* size_1))/venue_count) %>%
as.data.frame() %>%
select(d2_name, mean_experimentation, mean_creative_output, mean_community_focus, venue_dens, med_size, venue_count, mean_promotion) %>%
gather(-d2_name, key = "variable", value = "value"), .) %>%
mutate(value = as.numeric(value)) %>%
mutate(quantile = case_when(value >= `75%` ~ 4,
value < `25%` ~ 1,
value >= `25%` & value < `50%` ~ 2,
value >= `50%` & value < `75%` ~ 3)) %>%
mutate(variable = case_when(variable == "mean_experimentation" ~ "Experimental Content",
variable == "venue_dens" ~ "Venue Density",
variable == "venue_count" ~ "Total Venues",
variable == "mean_promotion" ~ "Promotion of\n Artistic Content",
variable == "med_size" ~ "Median Venue Size",
variable == "mean_creative_output" ~ "Creative Content",
variable == "mean_community_focus" ~ "Community Focus")) %>%
ggplot()+
geom_tile(aes(y = d2_name, x = variable, fill = quantile),
alpha = 0.6)+ #flip coord, order desc
scale_fill_gradient(low = "white", high = CityPalette[5],
limits=c(1,4), breaks=c(1,4),
labels=c("Minimum","Maximum"),
name = "")+
coord_flip()+
labs(
title = "District Venue Characteristics",
subtitle = "",
caption = "Data: CFP, City of Copenhagen, Resk (2020), Finans Danmark, OpenData DK")+
#ylab("Quartile")+
#xlab("")+
plotTheme
8 Supervized Classification Algorithms for Programming and Geographic Clusters
Experimental approach using a classifying clustering algorithm to determine geographic “neighborhoods” of nightlife amenities (geo-clusters) and thematic groupings of venues “program clusters”.
The geographic clustering approach is again, rather inconclusive, so it has been retired.
8.1 Geo-clusters
This first test is a “k-means” cluster analysis that is strictly looking for geographic clusters - this analysis functions by partitioning a cloud of points in multi-dimensional data to find cluster centroids, and assign each point to a cluster by minimizing within cluster variance.
set.seed(123)
library(cluster)
library(factoextra)
# function to compute total within-cluster sum of square
wss <- function(k) {
kmeans(main_venue_data %>%
filter(city == "Copenhagen",
is.na(y) == FALSE) %>%
select(x, y), k, nstart = 10 )$tot.withinss
}
# Compute and plot wss for k = 1 to k = 15
k.values <- 1:15
# extract wss for 2-15 clusters
wss_values <- map_dbl(k.values, wss)
# Run "elbow plot" to determine optimal cluster number.
plot(k.values, wss_values,
type="b", pch = 19, frame = FALSE,
xlab="Number of clusters K",
ylab="Total within-clusters sum of squares")
# Create a data object with cluster numbers
test <- kmeans(main_venue_data %>%
filter(city == "Copenhagen" &
is.na(y) == FALSE) %>%
select(x, y), centers = 4, nstart = 25)This interactive map shows the location of these clusters:
cluster_data1 <- main_venue_data %>%
filter(city == "Copenhagen",
is.na(y) == FALSE) %>%
cbind(test$cluster) %>%
rename(cluster = 'test$cluster')
pal <- colorNumeric(c("red", "blue", "green"), 1:3)
l2 <- leaflet() %>%
addProviderTiles(providers$Esri.WorldTopoMap) %>%
setView(lng = mean(cluster_data1$x, na.rm = TRUE),
lat = mean(cluster_data1$y, na.rm = TRUE),
zoom = 11) %>%
addScaleBar(position = "topleft") %>%
addCircleMarkers(data= cluster_data1,
lng=~x,
lat=~y,
radius =~ 1,
fillOpacity =~ 1,
color = ~pal(cluster),
label=~paste(name, street, " | Cluster: ", cluster))
l28.1.1 Geo cluster programming characteristics
main_venue_data %>%
filter(city == "Copenhagen",
is.na(y) == FALSE) %>%
mutate(years_operating = as.numeric(years_operating)) %>%
cbind(test$cluster) %>%
rename(cluster = 'test$cluster') %>%
filter(is.na(years_operating) == FALSE) %>%
group_by(cluster) %>%
summarize(mean_size = mean(size),
mean_promotion = mean(promotion),
mean_creative_output = mean(creative_output),
mean_experimentation = mean(experimentation),
mean_community_focus = mean(community_focus),
mean_age = mean(years_operating)) %>%
gather(-cluster, key = "variable", value = "value") %>%
ggplot()+
geom_bar(aes(x = cluster, y = value, fill = as.factor(variable)),
stat = "identity", position = "dodge")+
labs(
title = "Venue Typologies",
subtitle = "k = 4 geographic clusters",
caption = "Data: CFP")+
#ylab("Quartile")+
plotTheme8.2 Programming Clusters
There are three programming clusters in the venue sample. They are characterized as follows:
Creative engines - Medium-to small venues, of median age (~3-10 years) with the most highly rated programming. Located in mor outlying areas. (The second largest cluster)
Mainstream - Large venues, of median age, with above below average programming rankings. (The smallest cluster)
Copenhagen legacy venues - larger venues with high programming rankings, on average, much older with below average programming rankings. Venues in this category tend to be much lower ranked in other cities. (The largest cluster)
set.seed(123)
library(cluster)
library(factoextra)
# function to compute total within-cluster sum of square
wss2 <- function(k) {
kmeans(main_venue_data %>%
filter(city == "Copenhagen",
is.na(size) == FALSE,
is.na(promotion) == FALSE,
is.na(creative_output) == FALSE,
is.na(experimentation) == FALSE,
is.na(community_focus) == FALSE,
is.na(years_operating) == FALSE) %>%
mutate(years_operating = as.numeric(years_operating)) %>%
filter(is.na(years_operating) == FALSE) %>%
select(size, promotion, creative_output, experimentation, community_focus, years_operating), k, nstart = 10 )$tot.withinss
}
# Compute and plot wss for k = 1 to k = 15
k.values2 <- 1:15
# extract wss for 2-15 clusters
wss_values2 <- map_dbl(k.values2, wss2)
# Run "elbow plot" to determine optimal cluster number.
plot(k.values2, wss_values2,
type="b", pch = 19, frame = FALSE,
xlab="Number of clusters K",
ylab="Total within-clusters sum of squares")
# Create a data object with cluster numbers
test2 <- kmeans(main_venue_data %>%
filter(city == "Copenhagen",
is.na(size) == FALSE,
is.na(promotion) == FALSE,
is.na(creative_output) == FALSE,
is.na(experimentation) == FALSE,
is.na(community_focus) == FALSE,
is.na(years_operating) == FALSE) %>%
mutate(years_operating = as.numeric(years_operating)) %>%
filter(is.na(years_operating) == FALSE) %>%
select(size, promotion, creative_output, experimentation, community_focus, years_operating), centers = 3, nstart = 25)
main_venue_data %>%
filter(city == "Copenhagen",
is.na(size) == FALSE,
is.na(promotion) == FALSE,
is.na(creative_output) == FALSE,
is.na(experimentation) == FALSE,
is.na(community_focus) == FALSE,
is.na(years_operating) == FALSE) %>%
mutate(years_operating = as.numeric(years_operating)) %>%
filter(is.na(years_operating) == FALSE) %>%
cbind(test2$cluster) %>%
rename(cluster = 'test2$cluster') %>%
group_by(cluster) %>%
summarize(med_size = median(size),
med_promotion = median(promotion),
med_creative_output = median(creative_output),
med_experimentation = median(experimentation),
med_community_focus = median(community_focus),
med_age = median(years_operating)) %>%
gather(-cluster, key = "variable", value = "value") %>%
ggplot()+
geom_bar(aes(x = variable, y = value), stat = "identity")+
facet_wrap(~cluster)+
labs(
title = "Venue Typologies",
subtitle = "k = 3 clusters based on normalized programming metrics",
caption = "Data: CFP")+
#ylab("Quartile")+
plotTheme
main_venue_data %>%
filter(city == "Copenhagen",
is.na(size) == FALSE,
is.na(promotion) == FALSE,
is.na(creative_output) == FALSE,
is.na(experimentation) == FALSE,
is.na(community_focus) == FALSE,
is.na(years_operating) == FALSE) %>%
mutate(years_operating = as.numeric(years_operating)) %>%
filter(is.na(years_operating) == FALSE) %>%
cbind(test2$cluster) %>%
rename(cluster = 'test2$cluster') %>%
group_by(cluster) %>%
tally() %>%
ggplot()+
geom_bar(aes(x = cluster, y = n), stat = "identity")+
labs(
title = "Total Venues per Typology",
subtitle = "k = 3 clusters based on normalized programming metrics",
caption = "Data: CFP")+
plotTheme
cluster_data2 <- main_venue_data %>%
filter(city == "Copenhagen",
is.na(size) == FALSE,
is.na(promotion) == FALSE,
is.na(creative_output) == FALSE,
is.na(experimentation) == FALSE,
is.na(community_focus) == FALSE,
is.na(years_operating) == FALSE) %>%
mutate(years_operating = as.numeric(years_operating)) %>%
filter(is.na(years_operating) == FALSE) %>%
cbind(test2$cluster) %>%
rename(cluster = 'test2$cluster')
cluster_data2 %>%
mutate(cluster = as.factor(cluster)) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
mapView(., zcol = "cluster" )+
mapview(d2_aggregates %>%
select(d2_name))cluster_data2 %>%
mutate(cluster = as.factor(cluster)) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_join(d2_aggregates %>% st_transform(4326)) %>%
as.data.frame() %>%
group_by(d2_name, cluster) %>%
tally() %>%
spread(key = cluster, value = n) %>%
filter(is.na(d2_name) == FALSE) %>%
rename (creative_engines = `1`,
mainstream = `2`,
legacy = `3`)## # A tibble: 10 × 4
## # Groups: d2_name [10]
## d2_name creative_engines mainstream legacy
## <chr> <int> <int> <int>
## 1 Amager Øst 2 1 2
## 2 Amager Vest 1 NA 3
## 3 Bispebjerg 5 2 1
## 4 Brønshøj-Husum 1 NA NA
## 5 Indre By 10 10 21
## 6 Nørrebro 8 NA 9
## 7 Østerbro 1 5 2
## 8 Valby 1 1 1
## 9 Vanløse 2 1 NA
## 10 Vesterbro/Kongens Enghave 5 6 59. Key district profiles
Sources: CFP, Rezk(2020), City of Copenhagen: Statbank, Finans Danmark
profiles <- d2_aggregates %>%
mutate(pop_t2 = as.numeric(pop_t2),
pop_t1 = as.numeric(pop_t1),
pop_youth_t1 = as.numeric(pop_youth_t1),
pop_youth_t2 = as.numeric(pop_youth_t2),
rent_t1 = as.numeric(rent_t1)/1000,
rent_t2 = as.numeric(rent_t2)/1000,
income_t1 = as.numeric(income_t1),
income_t2 = as.numeric(income_t2)) %>%
mutate(pop_pct_change = (pop_t2 - pop_t1)/pop_t1 *100, pop_youth_pct_change = (pop_youth_t2 - pop_youth_t1) / pop_youth_t1 *100,
pct_change_rent = (rent_t2 - rent_t1) / rent_t1 * 100,
pct_change_income = (income_t2 - income_t1) / income_t1 *100, venue_per_km2 = venue_count / area_km2,
med_size = ((4* size_4) + (3*size_3) + (2* size_2) + (1* size_1))/venue_count) %>%
select(d2_name, pop_t1, pop_t2, pop_pct_change, pop_per_km2_t1, pop_per_km2_t2, pop_youth_t1, pop_youth_pct_t1, pop_youth_t2, pop_youth_pct_t2, pop_youth_pct_change, pop_youth_per_km2_t1, pop_youth_per_km2_t2, pop_per_venue_t1, pop_youth_per_venue_t1, pop_per_venue_t2, pop_youth_per_venue_t2, rent_t1, rent_t2, change_rent, pct_change_rent, income_t1, income_t2, change_income, pct_change_income, station_count, transit_stations_per_km2, venue_count, venue_per_km2, med_size, venue_count, mean_promotion, mean_experimentation, mean_creative_output, mean_community_focus) %>%
st_drop_geometry()
write_csv(profiles, "~/GitHub/CFP/unified__city_data/copenhagen/district_profiles/profiles_table.csv")
profiles %>%
select(d2_name, pop_t2,rent_t2, income_t2, med_size, transit_stations_per_km2,
venue_count, mean_promotion, mean_experimentation,
mean_creative_output, mean_community_focus ) %>%
left_join(., cluster_data2 %>%
mutate(cluster = as.factor(cluster)) %>%
st_as_sf(coords = c("x", "y"), crs = 4326) %>%
st_join(d2_aggregates %>% st_transform(4326)) %>%
as.data.frame() %>%
group_by(d2_name, cluster) %>%
tally() %>%
spread(key = cluster, value = n) %>%
filter(is.na(d2_name) == FALSE) %>%
rename (creative_engines = `1`,
mainstream = `2`,
legacy = `3`)) %>%
mutate(across(where(is.numeric), ~ signif(., digits = 4))) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "650px", height = "400px")| d2_name | pop_t2 | rent_t2 | income_t2 | med_size | transit_stations_per_km2 | venue_count | mean_promotion | mean_experimentation | mean_creative_output | mean_community_focus | creative_engines | mainstream | legacy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Vanløse | 40660 | 36.62 | 382600 | 2.667 | 0.7502 | 3 | 3.000 | 3.000 | 3.000 | 2.333 | 2 | 1 | NA |
| Valby | 65890 | 38.01 | 367800 | 3.667 | 0.8707 | 3 | 2.667 | 2.667 | 3.000 | 3.333 | 1 | 1 | 1 |
| Brønshøj-Husum | 43930 | 31.06 | 349900 | 4.000 | 0.1150 | 1 | 4.000 | 3.000 | 4.000 | 3.000 | 1 | NA | NA |
| Indre By | 56810 | 58.18 | 504600 | 2.786 | 0.9598 | 42 | 3.000 | 2.452 | 3.190 | 2.429 | 10 | 10 | 21 |
| Østerbro | 80660 | 51.79 | 427600 | 3.667 | 0.8228 | 9 | 2.333 | 2.222 | 2.667 | 1.556 | 1 | 5 | 2 |
| Vesterbro/Kongens Enghave | 78890 | 53.36 | 405400 | 2.875 | 1.3380 | 16 | 2.812 | 2.812 | 3.000 | 2.500 | 5 | 6 | 5 |
| Amager Øst | 62100 | 42.16 | 373500 | 3.400 | 0.5378 | 5 | 3.000 | 2.200 | 3.200 | 1.800 | 2 | 1 | 2 |
| Amager Vest | 86350 | 42.16 | 386100 | 3.750 | 0.3114 | 4 | 3.500 | 2.250 | 3.750 | 2.000 | 1 | NA | 3 |
| Bispebjerg | 54240 | 37.73 | 317300 | 2.375 | 0.5886 | 8 | 3.000 | 2.375 | 3.375 | 3.000 | 5 | 2 | 1 |
| Nørrebro | 78510 | 48.65 | 330700 | 2.235 | 0.7341 | 17 | 3.235 | 3.118 | 3.647 | 3.235 | 8 | NA | 9 |
9.1. Indre By
Indre By, the historic city center of Copenhagen, is a vibrant and compact district situated on a small island surrounded by picturesque canals. Dating back to the 12th century, it has evolved from a fishing village into a bustling market town, witnessing significant historical events and architectural transformations. Landmark attractions such as the Rundetaarn, Rosenborg Castle, and the Church of Our Saviour highlight its rich heritage. Indre By boasts the highest number of music venues in the sample, with 42 spaces that cater to various tastes, including “Legacy,” “Creative Engine,” and “Mainstream” venues.
While the district has relatively high content scores for a city center, indicating a dynamic cultural scene, it faces challenges due to high housing prices, which limits venues’ growth potential. Enhanced by excellent public transit access, Indre By attracts both locals and tourists, offering a unique blend of historical significance and modern vibrancy. This eclectic mix fosters creativity, but the economic pressures can hinder the development of new cultural spaces, making it crucial to find a balance between maintaining its rich heritage and nurturing the creative community.
9.2. Nørrebro
Nørrebro is a dynamic and diverse district in north of Indre By, known for its multicultural atmosphere and vibrant street life. With a rich history that includes significant working-class roots, Nørrebro has transformed into a hub of creativity and social engagement. The area features a mix of historic architecture and modern developments, with popular landmarks like the Assistens Cemetery, where notable figures such as Hans Christian Andersen are buried, and the lively Nørrebrogade, lined with shops and cafes.
Nørrebro is also home to a thriving cultural scene. Nørrebro boasts the second-highest number of music venues in the city, with 17 spaces - yet it lacks mainstream venues, highlighting its focus on alternative and independent programming. The district enjoys very high program scores, reflecting its vibrant artistic community. Rents here are not increasing as rapidly as in the city center, growing 17% slower than Indre By, making it a more affordable option for residents and creative ventures. As gentrification looms, Nørrebro is positioned as an ideal location for affordability controls and preservation efforts. The 2019 Kommunenplan specifically identifies envisions Nordvest as a burgeoning area for creative entrepreneurship.
9.3. Vesterbro/Kongens Enghave
Vesterbro is a vibrant district situated just west of Copenhagen’s city center, known for its eclectic mix of culture and nightlife. It features a blend of residential neighborhoods and bustling streets, making it a popular destination for both locals and tourists. Historically, Vesterbro was a working-class area that underwent significant transformation in the late 20th century, evolving into a hub of creativity and entertainment. Key landmarks include the iconic Tivoli Gardens, a historic amusement park, and Istedgade, renowned for its unique shops and eateries.
With 16 music venues, Vesterbro has above-average venue density, contributing to its lively cultural scene. The district’s program scores are average, indicating a solid array of offerings. Property values are relatively high, reflecting the area’s desirability, while the average income in Vesterbro stands at approximately 405,363 DKK, suggesting a well-off demographic. Property values are relatively high. The district benefits from high transit density, making it easily accessible.
Currently, Vesterbro is undergoing significant transformation, particularly around its industrial areas, such as the former meatpacking district, now reimagined as a creative hub with galleries, restaurants, and event spaces. This shift has fostered new cultural opportunities while maintaining a relative balance between different venue types, enhancing the district’s cultural diversity and appeal.
9.4. Østerbro
Østerbro is an affluent district located to the northeast of Copenhagen’s city center, characterized by its spacious parks, quiet residential streets, and proximity to the waterfront. Historically, Østerbro has evolved from a primarily working-class area into a desirable neighborhood known for its family-friendly environment and green spaces. Notable landmarks include the stunning Fælledparken, the largest park in Copenhagen, and the iconic St. Alban’s Church, a striking example of neo-Gothic architecture.
With 9 music venues, Østerbro boasts above-average venue density, and the median venue size is notably large, allowing for a variety of events and performances. However, the district has low program scores, owing to its concentration of Mainstream programming. Property values in Østerbro are relatively high, with housing prices averaging around 51,794 DKK per square meter, relatively high comparing to local residents’ income. The district is currently undergoing significant waterfront redevelopment, enhancing its appeal and accessibility. It is reasonable to expect that this development, along with the potential arrival of mainstream venues, will continue unless affordability controls are implemented. The 2019 Kommunenplan highlights Outer Nordhavn as a key area where industrial buildings can be repurposed for creative uses, further contributing to Østerbro’s evolving cultural landscape.
Appendix - Experimental Regression Analysis
How is venue density predicted by transit density (controlling for city)?
This explains roughly 20% of the variance in district venue density.
d1_aggregates %>%
as.data.frame() %>%
filter(city_en %in% c("Tokyo", "Berlin", "Montreal", "Stockholm")) %>%
filter(! d1_name %in% remove_arrondissements) %>%
mutate(venue_dens = venue_count / area_km2,
venue_dens = ifelse(venue_dens == 0, 0, venue_dens),
city = case_when(city_en == "Berlin" ~ "1. BERLIN, 2017",
city_en == "Tokyo" ~ "3. TOKYO, 2019",
city_en == "Stockholm" ~ "4. STOCKHOLM, 2021",
city_en == "Montreal" ~ "5. MONTREAL, 2022")) %>%
select(city, venue_dens, transit_stations_per_km2) %>%
rbind(., d2_aggregates_all %>%
as.data.frame() %>%
filter(city_en %in% c("Sydney", "New York", "Rotterdam")) %>%
mutate(venue_dens = venue_count / area_km2,
venue_dens = ifelse(venue_dens == 0, 0, venue_dens),
city = case_when(city_en == "New York" ~ "2. NEW YORK, 2018",
city_en == "Sydney" ~ "6. SYDNEY, 2023",
city_en == "Rotterdam" ~ "7. ROTTERDAM, 2024")) %>%
select(city, venue_dens, transit_stations_per_km2)) %>%
rbind(., d2_aggregates %>%
as.data.frame() %>%
mutate(venue_dens = venue_count / area_km2,
venue_dens = ifelse(venue_dens == 0, 0, venue_dens),
city = "8. COPENHAGEN, 2024") %>%
select(city, venue_dens, transit_stations_per_km2)) %>%
lm(data = ., venue_dens ~ transit_stations_per_km2 + city) %>%
summary(.)##
## Call:
## lm(formula = venue_dens ~ transit_stations_per_km2 + city, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.2732 -1.4826 -0.6306 0.0195 27.1161
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.56424 1.16027 0.486 0.62743
## transit_stations_per_km2 1.40352 0.25056 5.602 9.29e-08 ***
## city2. NEW YORK, 2018 -0.09615 1.24584 -0.077 0.93858
## city3. TOKYO, 2019 -1.20310 1.45622 -0.826 0.40996
## city4. STOCKHOLM, 2021 -0.41117 1.94280 -0.212 0.83266
## city5. MONTREAL, 2022 0.79504 1.63055 0.488 0.62652
## city6. SYDNEY, 2023 3.81366 1.45100 2.628 0.00943 **
## city7. ROTTERDAM, 2024 0.24548 1.77552 0.138 0.89021
## city8. COPENHAGEN, 2024 -0.16293 1.66990 -0.098 0.92240
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.821 on 157 degrees of freedom
## (154 observations deleted due to missingness)
## Multiple R-squared: 0.2385, Adjusted R-squared: 0.1997
## F-statistic: 6.148 on 8 and 157 DF, p-value: 6.825e-07Appendix - Methods and Sources
Methodology
This section describes the analysis methods used throughout the study.
Districts
Cities have various levels of administrative and cultural districts, from the block to the entire municipal boundary, which impact people’s urban experience and define jurisdictions and decision making powers. Three district levels that are based on statistical census areas as well as legislative areas are used in the analysis:
d_1. City of Copenhagen form the broadest level of aggregation to align analysis with decision making bodies.
d_2. 10 districts based on Census Statistical Areas are used to generate insights at the district scale, which also corresponds to nationally recognized census boundaries. This analysis was undertaken at the d_2 level.
d_3. Zip code areas are the smallest spatial unit of research and analysis.
Census
Demographic data were obtained for two time periods (t1, t2). The variables used include:
- pop_t1 & pop_t2: population of each district (2017, 2022)
- pop_youth_t1 & pop_youth_t2: population of the 20-29 years age group (2017, 2022)
- income_t1 & income_t2: Income for persons(14 years+) by district. Personal income in total (ex. imputed rent and before deductions of interest expenses). In DKK. Table KKIND3 (2017, 2022)
- rent_t1 & rent_t2 : Property prices on the housing market by district, property category and prices of completed transactions. For condominiums – mean market price, DKK/square meter. Table UDB020. (2017, 2022)
Limitations: 1. d1 and d3 levels of demographic data are not as accessible as other CFP cities, while only d2 level is used for quantitative analysis. 2. Condominiums takes the major residential housing type in Copenhagen. Due to limit of access to data sources, Condominium is taking as a subject of study for property values.
Transportation
Public transit networks provide insights into the varying levels of accessibility that exist within cities.61 stations composed of stations for lines M1, M2, M3, and M4 of Copenhagen metro and stations for the S-train.
Venues
Venue data is collected through CFP research and local workshops. 108 venues have been identified and ranked by community members according to a variety of metrics that assess venue features (type, age, social media presence), spatial characteristics (address, location, accessibility, size), and programming (number of events, number of uses, experimentation, creative output).
Data aggregation
Final data aggregation combines districts, census, transportation and venue data into a single data set, which contains the unique demographic, transportation and venue information for each borough. Additional variables are calculated to assess trends like venue density and relationships between venues, demographics and transit.
Sources
All venue data, which includes the selected venues and their corresponding geographic information, characteristics and rankings, were obtained through CFP research and local workshops throughout 2024.
Geographic district boundaries were obtained from Open Data DK in year 2024.
https://www.opendata.dk/city-of-copenhagen/bydele
- Transportation data, which includes 2024 train station entrance locations, were obtained from Clustering of Copenhagen Stations research conducted by Anas Rezk. The data was released on May 27, 2020.
- Demographic data were obtained from the City of Copenhagen: Statbank for years 2017 and 2022
References to planning initiatives in Copenhagen relate to the following documents:
CITATIONS FOR DOCUMENTS
City of Copenhagen. (2019). KommunenPlan 2019 Copenhagen’s municipal plan 2019. City of Copenhagen. Retrieved from https://kp19.kk.dk/copenhagen-municipal-plan-2019
Madsen, M. D., Paasch, J. M., & Sørensen, E. M. (2022). The many faces of condominiums and various management structures − The Danish case. Land Use Policy, 120, 106273. https://doi.org/10.1016/j.landusepol.2022.106273